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Optimizing technical indicators with Kelly criterion
Master’s Thesis
Miiro Nygrén
Aalto University School of Business
Finance
Fall 2020/Spring 2021
Aalto University, P.O BOX 11000, 00076
AALTO
www.aalto.fi
Abstract of master’s thesis
Tekijä Miiro Nygrén
Työn nimi Optimizing technical indicators with Kelly criterion
Tutkinto Kauppatieteiden maisteri
Koulutusohjelma Rahoitus
Työn ohjaaja(t) Matthijs Lof
Hyväksymisvuosi 2021 Sivumäärä 35 Kieli Englanti
Tiivistelmä
Tekniset indikaattorit eivät tuota tilastollisesti merkitsevää positiivista alpha Fama-French 5-
osatekijän regressiossa valitulla tietoaineistolla, joka sisältää 36 maakohtaista ETF, jotka on valittu
kehittyvistä ja kehittyneistä maista. Tilastollisina poikkeuksina tähän olivat Peru ja Thaimaa.
Varojen hallinta näkökulman lisääminen Kelly kriteerin, joka maksimoi loppu pääoman odotetun
geometrisen kasvun, avulla ei myöskään tuota tilastollisesti merkitsevää positiivista alphaa,
vaikkakin Kelly kriteeri parantaa tulosta kehittyneillä markkinoilla, missä yksittäiset tekniset
indikaattorit eivät toimineet yhtä hyvin kuin kehittyvillä markkinoilla. Useiden eri maiden
yhdistäminen ja yksittäisen teknisen indikaattorin käyttäminen yhdessä Kelly kriteerin kanssa ei
myöskään tuottanut tilastollisesti merkitseviä tuloksia ja useimmissa tapauksissa johti korkean
vipuvaikutuksen takia strategian menettämään kaikki varat.
Avainsanat Kelly kriteeri, Tekninen indikaattori, kehittyvät markkinat, kehittyneet markkinat,
markkinoiden tehokkuus
Aalto University, P.O BOX 11000, 00076
AALTO
www.aalto.fi
Abstract of master’s thesis
Author Miiro Nygrén
Title of thesis Optimizing technical indicators with Kelly criterion
Degree Master of Science in Economics and Business Administration
Degree programme Finance
Thesis advisor(s) Matthijs Lof
Year of approval 2021 Number of pages 35 Language English
Abstract
Technical indicators do not generate statistically significant positive alpha in Fama-French 5-factor
regression in the selected data set of 36 country-specific ETFs, located in both emerging market and
developed markets, with two statistical outliers being Peru and Thailand. The addition of money
management aspect through the Kelly criterion, which is mathematically maximizing the expected
geometric growth rate of the end value, does not generate statistically significant results either,
though the Kelly criterion does improve performance in developed markets in which individual
technical indicators do not perform as well as in emerging markets. Combining multiple different
countries with a single technical indicator in a combined portfolio that is then optimized using the
Kelly criterion does not provide statistically significant results and, in most cases, due to high
leverage ended up with the portfolio losing everything.
Keywords Kelly criterion, Technical indicators, Emerging market, Developed market, Market
efficiency
1
Table of Contents
1. Introduction ..............................................................................................................................2
1.1. Summary of the study’s results.................................................................................................2
1.2. Structure of the study ...............................................................................................................3
2. Prior literature ..........................................................................................................................4
2.1. Efficient market hypothesis and general usefulness of technical indicators ...................................4
2.2. Specific technical indicators ....................................................................................................6
2.3. Kelly criterion .........................................................................................................................8
3. Methods and data ......................................................................................................................9
3.1. Data ........................................................................................................................................9
3.2. Methodology ......................................................................................................................... 11
3.2.1. Performance measures ...................................................................................................... 11
3.2.1.1. Performance and annualized performance ..................................................................... 12
3.2.1.2. Simple buy-and-hold index ............................................................................................. 12
3.2.1.3. Profit and loss index ...................................................................................................... 12
3.2.1.4. Reward and risk index ................................................................................................... 12
3.2.1.5. Sharpe ratio ................................................................................................................... 13
3.2.1.6. Average profit/average loss ........................................................................................... 13
3.2.1.7. Percentage of profitable trades ...................................................................................... 13
3.2.2. Testing statistics ................................................................................................................ 14
3.3. Technical indicators .............................................................................................................. 15
3.3.1. RSI .................................................................................................................................... 15
3.3.2. MACD ............................................................................................................................... 15
3.3.3. TRB ................................................................................................................................... 16
3.3.4. STOCH-D.......................................................................................................................... 16
3.3.5. OBV .................................................................................................................................. 17
3.4. Kelly criterion ....................................................................................................................... 17
3.5. Fama-French factors ............................................................................................................. 18
3.6. Emerging market efficiency hypothesis .................................................................................. 18
4. Results ..................................................................................................................................... 19
4.1. Absolute and risk-adjusted performance ................................................................................ 19
4.2. Statistic test and Fama-French 5-factor regression ................................................................ 24
5. Summary and conclusions ...................................................................................................... 30
References ....................................................................................................................................... 32
Appendix ......................................................................................................................................... 36
2
1. Introduction
Investing boils down to maximizing the expected end wealth and investors have always
been looking for different ways to give them an edge over their competition. This paper aims
to build upon existing research on the topic, by approaching the problem by combining a few
of the most researched and widely used technical analysis indicators and the Kelly criterion,
which maximizes the expected geometric growth rate of wealth, to combine an indicator that
gives buy and sell signals and money management aspect on each signal, thus maximizing the
chances of beating the buy-and-hold strategy. The motivation for this paper is both academic,
to build upon existing academic literature on technical analysis by bringing the most studied
indicators together and optimizing their results through the Kelly criterion, which determines
the optimal size of investment to maximize the expected logarithmic end value of wealth. In
addition, the motivation is highly practical, as technical analysis is used way more in practice
than would be expected by just looking at what academic literature says about the topic, and
this paper aims to bring an additional optimization aspect on technical analysis through money
management.
I test five different technical indicators in 36 country-specific stock market exchange-traded
funds (ETF) in an out-of-sample time period from 4th of January 2016 to 19th of June 2020,
after optimizing each of the technical indicators for Kelly criterion with data from an in-sample
period spanning from 1st of September 2011 to 31st of December 2015.
1.1. Summary of the study’s results
The study does not find any kind of definitive positive result from combining the
individual technical indicators with the Kelly criterion, outside of two statistical outliers being
Peru and Thailand, after accounting for risk and transaction costs. This result in itself is not
significant, but for the persistency of these excess returns found in Thailand even after several
studies, such as Yu et al. (2013), Gunasekarage and Power (2001) and Tharavanij, Siraprapasiri,
and Rajchamaha (2015), have shown there to be excess returns available before the out-of-
sample period used in this paper. Individual technical indicators manage to outperform their
respective buy-and-hold country-specific ETFs in most cases in both absolute and risk-adjusted
terms, but this result is mostly due to many of the buy-and-hold strategies in selected countries
resulting in negative total returns. Implementing the Kelly criterion does not improve
performance in most cases with emerging market economies but seems to improve the
performance in both absolute and risk-adjusted terms with some of the developed market
3
economies. However, most of the results are not statistically significant and after running the
Fama-French 5-factor regression almost all the statistically significant results are found only in
the emerging market economies. There are too few statistically significant results to draw strong
broad conclusions on the effectiveness of either individual technical trading rules or the
implementation of Kelly criterion using those same technical trading rules.
1.2. Structure of the study
The structure of this paper is as follows: First I provide a literature review of prior literature
on the usefulness of technical indicators and Kelly criterion in section 2, followed by section 3
in which I explain the methodology used in the study and provide specifics on the data used to
acquire the results. After, I explain in detail the results of my study in section 4 and finishing
with the conclusion and discussion in section 5.
4
2. Prior literature
2.1. Efficient market hypothesis and general usefulness of technical indicators
Technical indicators and their usefulness in the investment world has been a very
controversial topic that has seen many great research papers both for and against the usefulness
of this kind of indicators. The starting point that every technical analysis paper begins with is
that according to the efficient market hypothesis (Fama, 1970) current prices reflect all possible
available information and thus historical prices are useless at predicting future prices. Neftçi
(1991) goes a bit further and states that if we think of the economical and time series as
Gaussian, then the indicators have no prediction power, but if the prices are non-lineal, they do
have some level of prediction power.
Hull and McGroarty (2014) study market efficiency as well, but specifically in emerging
markets and they find out that as emerging markets develop, they become more efficient. Their
findings suggest that emerging markets should have more market inefficiencies and thus
technical trading rules should perform better in these countries. Another paper supporting this
conclusion is Bekaert and Harvey (2002) in which they summarize the academic evidence that
seems to suggest lower efficiency in emerging markets. However, Griffin, Kelly, and Nardari
(2010) also study market efficiency using post earnings drift and short-term reversal in 28
developed and 28 emerging market economies and do not find a significant difference in the
efficiencies between the two types of markets, suggesting that emerging market economies
would not be significantly less efficient than their developed counterparts.
Marshall, Cahan, and Cahan (2008) take a high-frequency data approach to the topic
and study if an intraday technical analysis has value in the U.S. equity market. Their study is
quite extensive as they test 7846 popular technical trading rules, but they cannot find that any
of these are profitable after accounting for data snooping bias. This finding is particularly
important as technical analysis is usually considered to be a shorter investment horizon tool
(Marshall et al. 2008).
Friesen, Weller, and Dunham (2009) try to provide a model that explains how technical
trading rules based on past prices succeed in generating excess profits. Their point is that traders
suffer from confirmation bias and interpret additional information after making a trade based
on acquired information in direction of their original view. Their model predicts positive
autocorrelation after sequential price jumps, and they do find in their test both economically
and statistically significant positive autocorrelation.
5
Shynkevich (2012) focuses on the growth and small-cap segments in his paper about
technical analysis in US equity markets and finds that in between 1995-2010 the first half of
that period yields better predictability, after accounting for data snooping, as well as statistically
significant superior returns, but on the second half of the period the predictability is already
disappeared suggesting that these segments of the US equity market have become much more
efficient over that specific period.
Menkhoff (2010) uses survey data from the US, Germany, Switzerland, Italy, and
Thailand to determine how widely technical analysis is being used by professional fund
managers and finds out that a very large majority (87%) considers technical analysis as at least
somewhat important in their decision making, while not offsetting the importance of
fundamental analysis as it acts a complimentary part of the decision-making tool kit of fund
managers. Menkhoff (2010) also finds that the users of the technical analysis view the
psychological factors influencing the prices and that these factors are the edge where technical
analysis performance stems from.
One possible angle of usefulness in implementing technical indicators in practice is not
so much the ability to time the market, but rather to neutralize the traders own behavioral biases.
One of these well-known biases is so call disposition effect, which means that individual
investors tend to sell winning stocks too early and hold on to their losing positions for too long
(Odean 1998). Here technical trading rules can help to counteract this bias by allowing the
profitable trades to run their course while cutting the losing trades earlier. If this bias is the
explanation for the success of the technical trading rule it should showcase a significant positive
asymmetry between the profits and losses in trades.
Blume, Easley, and O’Hara (1994) show in their study how accounting for volume can
be a useful addition to technical analysis as it provides additional and different information than
just the prices alone. Their findings are that combining volume and price can be informative for
investors, which leads to better performance.
Hsu, Hsu, and Kuan (2010) test in their paper a new stepwise superior predictive ability
test, which is an improvement upon Hansen's (2005) superior predictive ability test, which in
turn was developed to improve upon the reality check test. All these tests have the same end
goal of accounting for data snooping that arises when testing a large number of technical trading
rules, which could mean that some results come up statistically significant by pure chance as
researchers are testing thousands upon thousands of different rules in the same data set. Hsu,
Hsu, and Kuan (2010) test this method in growth and emerging markets in both market indices
and their respective ETF counterparts and find that there is strong evidence of significant
6
predictive ability for indices in pre-ETF periods and these are significantly weakened in post-
ETF periods.
Treynor and Ferguson (1984) take a very different approach when it comes to defending
the technical analysis and show in their paper that when past prices are combined with other
valuable information, they can then be used to achieve excess profits, but the underlying
nonprice information is the reason there is excess profit to be earned, but the price information
is the mechanism permitting the investor to efficiently exploit this information.
2.2. Specific technical indicators
Brock, Lakonishok, and LeBaron (1992) find by using moving average and trading
range breakout (TRB) as their technical indicators they manage to outperform buy-and-hold
strategy as well as create less volatile returns following buy orders than sell orders and sell
orders having negative return, making a strong case for the predictive power of their chosen
indicators over the 1897-1986 period in Dow Jones Index. Bassembinder and Chan (1998) build
upon Brock et al. (1992) research and argue that their result is not mutually exclusive with the
notion of market efficiency.
One of the most robust results in favor of technical trading rules comes from Han, Yang,
and Zhou (2013), who manages to beat a buy-and-hold strategy by using standard moving
average technical analysis applied to portfolios sorted by volatility. They find the difference
between their returns and buy-and-hold has little or negative exposures to the Fama-French
(1993) SMB and HML factors and especially in high volatility portfolios have their abnormal
returns beat what CAPM, Fama-French 3-factor model or momentum strategy suggests. Even
though the moving average strategy is a trend-following strategy like the momentum strategy,
they find little correlation between the two strategies. Additionally, their abnormal returns
cannot be explained away by market timing or trend following factors developed by Fung and
Hsieh (2001) and the returns are not sensitive to changes in sentiment, default nor liquidity risk.
Pruitt and White (1988) test the CRISMA (Cumulative Volume, Relative Strength,
Moving Average) trading system with data from 1976 to 1985 and manage to produce a
significant outperformance compared to buy-and-hold, even after adjusting for timing, risk, and
transaction costs.
Zhu and Zhou (2009) show moving average rules offering useful information in the
optimal portion of portfolio allocation to asset resulting in added value compared to an investor
7
following fixed allocation, trying to capitalize on either on the random-walk theory or the mean-
variance approach.
Yu et al. (2013) also study moving average and TRB rules from 1991 to 2008 in the
Southeast Asian stock market and find that in Malaysia, Thailand, Indonesia, and the
Philippines they produce statistically significant profits compared to simple buy-and-hold, but
these profits do not exceed the transaction costs, except in Thailand.
Ratner and Leal (1999) test simple moving average rule in emerging markets of Latin
America and Asia between 1982-1995 and find that this indicator can add value to investors in
some of these markets, while in others they find no strong evidence suggesting additional value.
Gunasekarage and Power (2001) test moving average trading rule in specifically South Asian
stock markets and find that they can outperform buy-and-hold strategy by using this method in
this market from 1990 to 2000.
Marshall, Young, and Rose (2006) study how the oldest form of technical analysis, the
candlestick charting, performs for Dow Jones Industrial Average from 1992 to 2002 and do not
find it being profitable. Lo, Mamaysky, and Wang (2000) study a large number of U.S. stocks
from 1962-1996 and find some chart patterns, such as head-and-shoulders and double-bottoms
seem to have some predictive power and thus can be value-adding tools for the investment
process.
Chong and Ng (2008) find they can produce statistically significant overperformance
compared to a buy-and-hold strategy, by using relative strength indicator (RSI) and moving
average convergence-divergence (MACD) rules in the FT30. Rosillo, de la Fuente, and Brugos
(2013) build upon Chong and Ng's findings and analyze Spanish market companies using RSI,
MACD, momentum, and stochastic oscillator (STOCH). They find the best results by using
RSI and stochastic rules, while MACD does not perform nearly as well and momentum rules
lose basically everything.
Tharavanij, Siraprapasiri, and Rajchamaha (2015) study RSI, STOCH, MACD,
directional movement indicator (DMI), and on-balance volume (OBV) in Asian markets
between 2000 and 2013. Their findings show in less mature markets, specifically in Thailand,
technical trading rules produce statistically significant profits exceeding the simple buy-and-
hold strategy after accounting for transaction costs but do not provide statistically significant
profits in more mature markets, specifically in Singapore and in the middle ground produces
statistically significant profits, that disappear after transaction costs.
8
2.3. Kelly criterion
Kelly Jr (1956) initially describes the theory on how to reinvest the capital through
multiple consecutive games, specifically in a lottery with two possible outcomes and positive
expected present value. Throp (1969) develops the Kelly criterion much further and adapted it
to other games such as Blackjack and Roulette for example, as well as the stock market and
financial derivatives. Vince (1990) builds upon the normal binary outcome of a typical Kelly
criterion and develops a criterion that considers multiple sets of outcomes for each game, the
so-called Vince criterion.
After Thorp (1969) brought Kelly criterion to the public spotlight in financial markets
there have been several studies building on the research. For example, Gehm (1983) and Balsara
(1992) apply the Kelly criterion to trading in commodity markets and Wu and Chung (2018)
adapt the Kelly criterion to option trading.
Many of the advocates for the Kelly criterion however also recognize its application in the
stock market warrants the use of so-called “fractional Kelly” which is a certain predetermined
fraction of the “whole” Kelly criterion allocation. Perhaps the best motivation and explanation
comes from Thorp (2006) in which he first mathematically explains how loss from optimal
value is much larger for relative “overbetting” than “underbetting” the optimal value. The
second thing Thorp points out in his paper is that when dealing with the stock market getting a
reliable estimate of mean returns is covered in uncertainties and is more likely to be too high
than too low.
9
3. Methods and data
3.1. Data
The data for this paper is acquired by using the Thomson Reuters Eikon Datastream
service. The risk-free rate data is from Kenneth R. French data library for US-based investor
and for multiple factor regression US factors are used for all combined countries as a
benchmark, developed excluding US factors are used for developed countries, whereas
emerging market factors are used for emerging markets and both these are also acquired from
Kenneth R. French data library. For the specific countries, the following ETFs are being used,
as seen in Table 1 below. The data is in USD return format and includes daily close, open, high,
and low price for each index as well as trading volume from 10th of November 2010 to 19th of
June 2020. With this diverse selection of target countries and a relatively long total period of
time the data includes significant up- and downtrends in different markets and manages to even
include the March market crash of 2020 due to the COVID-19 pandemic and the subsequent
lockdown measures, thus providing a breadth of different market conditions to test the different
trading rules in. Countries picked for this study and categorized as emerging markets have a
lower average annualized buy-and-hold returns than their developed market counterparts. All
the returns are taken from the perspective of a US-based investor, so the USD return format
also takes into account the relative appreciation and depreciation of the currencies that also
affects the returns here, thus the underperformance of the emerging market countries is partly
due to the relative weakness of their currencies against the USD during the testing period. The
descriptive statistics for the whole data set can be found in Appendix 79.
10
Table 1
ETFs used in the study. * is for a frontier market. Source: https://www.msci.com/market-classification. Referenced
27/10/2020. The average annualized return of the buy-and-hold strategy is calculated in an in-sample (1st of September
2011 to 31st of December 2015)/out-of-sample (4th of January 2016 to 19th of June 2020)/total period (1st of September
2011 to 19th of June 2020) and shown in the table respectively in order.
Country Name of the ETF used Buy-and-hold returns
Emerging markets
Taiwan iShares MSCI Taiwan ETF +0,28%/+19,03%/+9,40%
China SPDR S&P China ETF +5,64%/+16,55%/+11,05%
South Korea iShares MSCI South Korea ETF -1,05%/+9,76%/+4,30%
Turkey iShares MSCI Turkey ETF -3,09%/-6,52%/-4,85%
Malaysia iShares MSCI Malaysia ETF -15,19%/-3,45%/-9,42%
Vietnam* VanEck Vectors Vietnam ETF -3,94%/+1,57%/-1,18%
Peru iShares MSCI Peru ETF -19,60%/+16,01%/-3,15%
Philippines iShares MSCI Philippines ETF +14,66%/-1,58%/+6,10%
Thailand iShares MSCI Thailand ETF +0,19%/+11,10%/+5,59%
Mexico iShares MSCI Mexico ETF -2,38%/-7,06%/-4,79%
Egypt VanEck Vectors Egypt Index ETF -4,68%/-10,58%/-7,72%
Indonesia VanEck Vectors Indonesia Index ETF -11,73%/+2,25%/-4,89%
Brazil iShares MSCI Brazil ETF -27,66%/+27,62%/-3,49%
India Invesco India ETF +3,70%/+0,12%/+1,87%
South Africa iShares MSCI South Africa ETF -7,16%/+2,77%/-2,24%
Chile iShares MSCI Chile ETF -19,77%/-2,29%/-11,33%
Colombia Global X MSCI Colombia ETF -26,11%/-1,22%/-14,38%
Developed markets
New Zealand iShares MSCI New Zealand ETF +7,28%/+16,89%/+12,06%
Netherlands iShares Netherlands ETF +12,34%/+13,64%/+13,00%
Switzerland iShares Switzerland ETF +10,15%/+10,25%/+10,20%
Japan iShares MSCI Japan ETF +9,67%/+7,14%/+8,38%
Sweden iShares MSCI Sweden ETF +7,44%/+5,71%/+6,56%
Israel iShares MSCI Israel ETF +4,79%/+6,04%/+5,42%
Germany iShares MSCI Germany ETF +12,24%/+4,40%/+8,19%
Belgium iShares MSCI Belgium ETF +15,96%/+0,33%/+7,74%
Ireland iShares MSCI Ireland ETF +36,27%/+1,15%/+17,14%
Canada iShares MSCI Canada ETF -6,64%/+9,55%/+0,30%
France iShares MSCI France ETF +6,49%/+7,44%/+6,97%
Italy iShares MSCI Italy ETF +6,83%/+0,66%/+3,66%
Australia iShares MSCI Australia ETF -4,81%/+5,43%/+0,26%
Hong Kong iShares MSCI Hong Kong ETF +6,70%/+5,38%/+6,03%
United Kingdom iShares MSCI United Kingdom ETF +1,80%/-3,66%/-1,01%
Norway Global X MSCI Norway ETF -7,87%/+4,56%/-1,75%
Spain iShares MSCI Spain ETF -2,50%/-2,95%/-2,73%
Singapore iShares MSCI Singapore ETF -5,58%/-0,06%/-2,82%
Austria iShares MSCI Austria ETF -1,88%/+3,23%/+0,68%
11
3.2. Methodology
In this paper, I use five different technical indicators in 36 different markets in-sample
period from 1st of September 2011 to 31st of December 2015 to determine the necessary
parameters for the Kelly criterion. Of course, these numbers are not exact ex-post as they are
acquired using the ex-ante testing period, but they act as best estimators for each trading rule.
Therefore, I will be using the so-called half Kelly, that is a fraction of 0,5 Kelly as Throp (2006)
states “Estimates of 𝑚𝑒 in the stock market have many uncertainties and, in case of forecast
excess return, are more likely to be too high than too low.” Also, mathematically loss from
optimal value is less by “underbetting” the unobserved optimal Kelly than “overbetting”.
After getting the required parameters for the Kelly criterion, I test the technical trading
rules during an out-sample period from 4th of January 2016 to 19th of June 2020. The Kelly
criterion optimized portfolio is long-only, and the required cash position is determined by the
cumulative percentage of all the non-negative Kelly’s costs the risk-free rate. At the beginning
of the test period portfolio has zero allocation to the underlying asset and at the end date all
positions are closed. When a long-only portfolio receives a buy order from one of the technical
indicators it allocates a predetermined fraction of the whole portfolio to the asset at the closing
price of the day that the buy signal is generated. It holds the position until the same indicator
generates a sell order, and the position is closed at the closing price of the day that the sell signal
is generated. When the trading rule has a position in cash, it is not earning anything as the
trading rules require consistently high liquidity. Additionally, each of the technical indicators
acts independently of one another, meaning there are no restrictions on mixed signals between
different indicators and each indicator has its own fraction of the portfolio to allocate at each
time.
First, I analyze each ETF individually with each trading rule acting as an asset for Kelly
criterion and then take each technical trading rule individually and all 36 of the ETFs acting as
assets for Kelly criterion. Then I use each trading rule on emerging market countries and
developed market countries separately so that I can compare the usefulness of trading rules
between these two sets of countries.
3.2.1. Performance measures
To measure the performance of each indicator I use the same parameters Tharavanij,
Siraprapasiri, and Rajchamaha (2015) use in their paper, as their performance measures “…are
intuitive and widely monitored by actual traders…”, even though they acknowledge that these
12
indicators are not widely used in the academic world. This paper also uses some of the same
statistical methods Tharavanij et al. (2015) use in their study.
3.2.1.1. Performance and annualized performance
Performance measures the amount of net profits or losses generated by the trading rules
by the end of the testing period. Annualized performance is then calculated by raising the
performance to the power of 365 divided by the total amount of days in the testing period.
3.2.1.2. Simple buy-and-hold index
This index is meant to show the difference between a simple buy-and-hold strategy and
the performance of trading rule; thus, it gives a certain value directly indicating whether the
trading rule is outperforming or underperforming the buy-and-hold and by what margin.
However, this index does not state anything about the net profits as positive numbers simply
mean that the trading rule is outperforming the buy-and-hold strategy and not that they are
making positive net profits. This index is simply a comparative indicator stating which trading
strategy does better over the long run.
3.2.1.3. Profit and loss index
This index tells us the amount of profitable (unprofitable) trades ranging from -100 to
+100. The equation is the following equation 1
𝑃𝑟𝑜𝑓𝑖𝑡 𝑎𝑛𝑑 𝑙𝑜𝑠𝑠 𝑖𝑛𝑑𝑒𝑥 =𝑁𝑒𝑡 𝑝𝑟𝑜𝑓𝑖𝑡
𝑀𝑎𝑥(𝑇𝑟𝑎𝑑𝑒 𝑝𝑟𝑜𝑓𝑖𝑡, 𝑇𝑟𝑎𝑑𝑒 𝑙𝑜𝑠𝑠)∗ 100 ( 1 )
for example, if the index is at +30, it tells that the trading rule is producing net profits, but the
number of total losses is (100-30=70) 70 % of the total profits, and thus the net profits of the
trading rule are 30%. The opposite is true for negative numbers of the index. An index value of
+100 would mean that the trading rule generates only profits and never loses and -100 would
always lose and never generate profit.
3.2.1.4. Reward and risk index
This index gives us the relative reward compared to risk. Reward here is the total net
profits and risk is the total possible change, negative or positive, in the equity value from the
initial investment. The positive change here is measured by positive net profits and negative
change is calculated by the highest open drawdown (HOD), which calculates the maximum
13
distance from the initial investment during the testing period. This indicator also produces value
ranging from -100 to +100 and the equation is the following equation 2
𝑅𝑒𝑤𝑎𝑟𝑑 𝑎𝑛𝑑 𝑟𝑖𝑠𝑘 𝑖𝑛𝑑𝑒𝑥 =
𝑁𝑒𝑡 𝑝𝑟𝑜𝑓𝑖𝑡
[𝑀𝑎𝑥(𝑁𝑒𝑡 𝑝𝑟𝑜𝑓𝑖𝑡, 0) + 𝐻𝑂𝐷]∗ 100
( 2 )
here a number of +10 would tell us that the trading rule is producing positive net profits, but
the return is 10% of the amount of risk measured here by the possible changes, positive and
negative, in the equity value from the initial investment and reverse holds for negative values
of the index. An index value of +100 would mean that the trading rule generates always positive
net profit and there is never a principal loss during the testing period and -100 would indicate
that the trading rule incurred maximum possible loss while never making any profits.
3.2.1.5. Sharpe ratio
Sharpe ratio is a very common ratio often used in finance when comparing different
assets or strategies as it describes the amount of excess return generated per excess volatility,
or in other words unit of risk, taken. The Sharpe ratio used here is the revised one by Sharpe
(1994) as shown in equation 3
𝑆𝑎 =
𝑅𝑝 − 𝑅𝑓
𝜎𝑝
( 3 )
in which 𝑆𝑎 is the Sharpe ratio of the asset, 𝑅𝑝is the return of the asset, 𝑅𝑓is the risk-free rate
and 𝜎𝑝 stands for the standard deviation of the excess return over the risk-free rate. All the
numbers are annualized, and the standard deviation is annualized using a standard of 250
trading days per year estimation for all the markets and years used in the study.
3.2.1.6. Average profit/average loss
This indicator calculates the average profit from profitable ratio to average loss from
unprofitable trades. A higher number indicates a better trading rule, as better trading rules let
their winning trades “run” while cutting losing trades quickly.
3.2.1.7. Percentage of profitable trades
This number provides the proportion of the profitable trades. The higher the number is
the better the trading rule is at correctly predicting the price changes.
14
3.2.2. Testing statistics
I begin by calculating continuously compounding daily returns from the closing prices.
Technical indicators would generate the buy signals and when testing the buy signal, the chosen
daily returns would be all the daily returns buy signal has generated up until the position is
closed by the following sell signal. The average return of the tested strategy is thus calculated
by using the following equation 4
�̅� =
∑ 𝑟𝑖𝑖∈𝜙
𝑛 ( 4 )
in which �̅�~𝑁 (𝜇,𝜎2
𝑛) and 𝜙 is the union of all disjoint intervals generated by the buy signals.
Now I denote 𝜇𝑏𝑢𝑦 as the population means of the daily returns generated by the trading
rules buy signals. Additionally, I denote 𝜎𝑏𝑢𝑦 as the standard deviations of these daily returns.
One would expect the average returns to be positive for buy signals therefore I test this by
generating one-tailed hypotheses:
𝐻𝑦𝑝𝑜𝑡ℎ𝑒𝑠𝑖𝑠 0: 𝜇𝑏𝑢𝑦 = 0
𝐻𝑦𝑝𝑜𝑡ℎ𝑒𝑠𝑖𝑠 1: 𝜇𝑏𝑢𝑦 > 0
and test these hypotheses against each other using the following test statistic in equation 5
𝑍𝑏𝑢𝑦 =�̅�𝑏𝑢𝑦
(𝑆𝑏𝑢𝑦
√𝑛𝑏𝑢𝑦
)
, 𝑆𝑏𝑢𝑦 = √∑ (𝑟𝑖 − �̅�𝑏𝑢𝑦)2
𝑖∈𝜙𝑏𝑢𝑦
(𝑛𝑏𝑢𝑦 − 1) ( 5 )
in which 𝑛𝑏𝑢𝑦 is the number of days the long position is held. For this one-tailed test the
significance level is set at 10%, 5%, and 1%, and thus the critical Z values are 1,28, 1,645, and
2,33 respectively.
To take the transaction costs into account, I am using the same methodology
Bessembinder and Chan (1998) use in their similar tests, thus the additional return (𝜋)
generated by the trading rules compared to the simple buy-and-hold strategy is given by the
following equation 6
𝜋 = ∑ 𝑟𝑖
𝑛𝑏𝑢𝑦
𝑖=1
( 6 )
here once again the 𝑛𝑏𝑢𝑦 is the number of days the long position is being held, 𝑟𝑖 is the return
of the long position on the specific day i. When one divides the additional return (𝜋) calculated
above with the number of the buy signals in total, it gives the average additional return per
15
signal, or how Bessembinder and Chan (1998) express it, the round-trip break-even cost (C), as
shown in the equation 7
𝐶 =𝜋
𝑠𝑏𝑢𝑦 ( 7 )
in which the 𝑠𝑏𝑢𝑦 is the number of the buy signal generated by the trading rule in total and to
be truly profitable the breakeven cost (C) or the average additional return per signal must be
greater than the round-trip transaction cost calculated.
3.3. Technical indicators
Technical indicators used in this paper are Relative Strength Index (RSI), Moving
Average Convergence Divergence (MACD), Trading Range Breakout (TRB), Stochastic
Oscillator D-variation (STOCH-D), and On Balance Volume (OBV).
3.3.1. RSI
The Relative Strength Index (RSI) is an oscillator used to show the relative strength of
the asset price compared to movements in closing prices. This indicator is initially designed by
Welles (1978) and its value is determined by the following equation 8
𝑅𝑆𝐼𝑡(𝑛) =
∑ (𝑃𝑡−𝑖 − 𝑃𝑡−𝑖−1)1{𝑃𝑡−𝑖 > 𝑃𝑡−𝑖−1}𝑛−1𝑖=0
∑ |𝑃𝑡−𝑖 − 𝑃𝑡−𝑖−1|𝑛−1𝑖=0
∗ 100 ( 8 )
in which 𝑅𝑆𝐼𝑡 represents the relative strength at time t, 𝑃𝑡 is the value of the asset at time t and
n is the number of periods. For this study, I use the 14-day RSI, which is popularly used among
traders. The RSI itself gets a value between 0-100. I use a similar buy and sell order levels
Rosillo, de la Fuente, and Burgos (2013) use for their research, as they use the same method
indicated by the creator Welles (1978), which means that when RSI(n) is greater than 30 and
the RSI(n-1) is less or equal to 30, then buy order is generated. For sell order to occur RSI(n)
need to be greater than 70 and RSI(n-1) to be less or equal to 70.
3.3.2. MACD
The purpose of Moving Average Convergence Divergence (MACD) is to identify
moments when trends change. It is constructed by subtracting a longer period Exponential
Moving Average (EMA) from a shorter period EMA and the exact equation for MACD is the
following equation 9, for the EMA equation 10 and signal line for MACD equation 11
16
𝑀𝐴𝐶𝐷(𝑛) = ∑ 𝐸𝑀𝐴𝑘(𝑖) − ∑ 𝐸𝑀𝐴𝑑(𝑖)
𝑛
𝑖=1
𝑛
𝑖=1
( 9 )
𝐸𝑀𝐴𝑘(𝑖) = 𝛼 ∗ 𝑝(𝑖) + (1 − 𝛼) ∗ 𝐸𝑀𝐴𝑛(𝑖 − 1), 𝛼 =
2
1 + 𝑛 ( 10 )
𝑆𝑖𝑔𝑛𝑎𝑙 = 𝐸𝑀𝐴(𝑀𝐴𝐶𝐷, 𝑁3) ( 11 )
in which k=12 and d=26, n is the number of days, and p(i) is asset price on an ith day. For the
signal line, the N3 standard value is 9-days. 12-day and 26-day EMAs have been chosen as they
are most commonly used for MACD calculations (Murphy, 1999). For this indicator, a buy
signal is generated when MACD crosses over its own signal line, while a sell signal is generated
when MACD crosses under its own signal line.
3.3.3. TRB
Trading range break-out (TRB) captures the essence of support and resistance levels
within prices. The theory states that if price can penetrate one of these levels there should be
considerable drift beyond this level and therefore buy signals are generated when price
penetrates a local maximum that is a resistance level and sell signal is generated when price
penetrates a local minimum that is a support level. The different lengths of local maximum and
minimum prices used in this paper are the same as Brock et al. (1992) use in their study: 50,
150, and 200 days.
3.3.4. STOCH-D
Stochastic oscillator (STOCH-D) is another contrarian indicator that is supposed to
signal about the trend changing. This indicator gives values between 0% and 100%, and
anything above 80% is considered to be overbought and values of under 20% are considered
oversold. Mathematically the stochastic indicator is calculated by the following equation 12
%𝐾(𝑁1, 𝑁2) =
∑ [𝑃𝑡−𝑖 − 𝐿𝐿𝑡−𝑖(𝑁1)]𝑁2𝑖=0
∑ [𝐻𝐻𝑡−𝑖(𝑁1) − 𝐿𝐿𝑡−𝑖(𝑁1)]𝑁2𝑖=0
∗ 100 ( 12 )
in which 𝑃𝑡 is the closing price at time t, LL(N1) is the lowest low price of the previous N1
period and HH(N1) is the highest high price of the same N1 period. N2 is just the averaging
period of %K. I am using the standard values of 14 days for N1 and 1 day for N2. The D
variation of STOCH does not use a fixed band to generate buy and sell signals but instead
17
generates a buy signal when %K crosses over %D line, which represents the simple moving
averages of %K. The selling signal is generated when the %K crosses under %D.
Mathematically the %D line is calculated using the equation 13
%𝐷 = 𝑆𝑀𝐴[%𝐾(𝑁1, 𝑁2), 𝑁3] ( 13 )
in which SMA stands for simple moving average and N3 is the averaging period of %D and the
standard value for N3 is 3 days (Colby, 2003), which is also being used in this paper.
3.3.5. OBV
The fifth trading rule category considered in this paper is a volume-based indicator On
Balance Volume (OBV), which is supposed to be a leading indicator meaning changes in its
value should proceed larger trend changes in the asset. The way OBV is calculated is for every
day that the closing price is higher (lower) than the previous day’s closing price, then the
volume of the day in question is added (deducted) to the previous day's OBV and thus making
it today OBV. However, the magnitude of the price change in the underlying asset does not
matter, only the direction of the price movement does. This indicator generates a buy signal
when the OBV value crosses above its own N1 days Exponential Moving Average (EMA) and
a selling signal when it crosses below its own N1 days EMA. I will use the standard N1 value
here, which is 3 days (Colby, 2003).
3.4. Kelly criterion
Initially described by Kelly Jr (1956), but further developed and adapted to casino
games, but also in investments, by Thorp (1969, 2006) the Kelly criterion for investment
decision when there are multiple assets is the following equation 14
𝐹∗ = 𝐶−1(𝑀 − 𝑅) ( 14 )
in which F* is a vector of Kelly percentages of each asset, 𝐶−1 is the covariance inverse matrix
of the assets, M is the row vector of the mean return of assets and R is the vector of risk-free
return.
I first take every trading rule in a single ETF as of its own asset in a multiple asset
equation of Kelly criterion and optimize them based on their performance in-sample, to get the
required Kelly percentage for the combined strategy in an out-sample period. Negative Kelly
percentage indicators are ignored for the out-of-sample period due to the long-only restriction
in the portfolio. As explained in the methodology section I will be using half Kelly here, which
is achieved by multiplying all Kelly percentages by 0,5.
18
3.5. Fama-French factors
To find out if the strategy produces alpha, one must perform a factor regression. Here I
follow the standard methodology originally developed by Fama and French (1993) and later
built upon to five-factor model by Fama and French (2015), which added operating profitability
and investments as factors on top of the market factor, size factor, and value factor. This model
is written out in equation 15
𝑅(𝑡) − 𝑅𝑓(𝑡) = 𝛼 + 𝛽 (𝑅𝑚(𝑡) − 𝑅𝑓(𝑡)) + 𝑠 ∗ 𝑆𝑀𝐵(𝑡) + ℎ ∗ 𝐻𝑀𝐿(𝑡) + 𝑟
∗ 𝑅𝑀𝑊(𝑡) + 𝑐 ∗ 𝐶𝑀𝐴(𝑡) + 𝑒(𝑡)
( 15 )
in which R is the total return, 𝑅𝑓 is the risk-free rate, 𝛼 is the alpha, 𝛽 is the market factor
coefficient, s is the size factor coefficient, h is the value factor coefficient, r is profitability
coefficient, c is the investment coefficient and e is the zero-mean residual.
3.6. Emerging market efficiency hypothesis
This study provides a good opportunity to also perform an additional test on whether
emerging markets are significantly less efficient, as Bekaert and Harvey's (2002) research
seems to suggest. Here the hypothesis pair is the following for technical trading rules
𝐻𝑦𝑝𝑜𝑡ℎ𝑒𝑠𝑖𝑠 0: 𝑍𝐸𝑚𝑒𝑟𝑔𝑖𝑛𝑔 𝑚𝑎𝑟𝑘𝑒𝑡𝑠 − 𝑍𝐷𝑒𝑣𝑒𝑙𝑜𝑝𝑒𝑑 𝑚𝑎𝑟𝑘𝑒𝑡𝑠 = 0
𝐻𝑦𝑝𝑜𝑡ℎ𝑒𝑠𝑖𝑠 1: 𝑍𝐸𝑚𝑒𝑟𝑔𝑖𝑛𝑔 𝑚𝑎𝑟𝑘𝑒𝑡𝑠 − 𝑍𝐷𝑒𝑣𝑒𝑙𝑜𝑝𝑒𝑑 𝑚𝑎𝑟𝑘𝑒𝑡𝑠 > 0
19
4. Results
The individual technical trading rules do not produce statistically significant positive returns
in this data set, with two statistical outliers being Peru and Thailand. The addition of the money
management aspect through the Kelly criterion does not improve the performance generally
and in a significant portion of the data set results in total loss of capital due to the high leverage
suggested. In this section, I first show the aggregated results from the study and discuss their
interpretations, as well as their implications and finally link them to existing literature. The
more detailed and country-specific statistics and results can be found in Appendixes 1-72.
4.1. Absolute and risk-adjusted performance
After getting the results from each of the technical trading rules I implement the Kelly
criterion using equation 14 and get the portfolio weights for each country as seen in Appendix
74. Following getting the results of the Kelly criterion in each of the 36 country-specific ETFs,
as listed in Table 1, I compare all the results against each of their respective buy-and-hold
strategies in terms of absolute performance in both in- and out-of-sample periods as seen in
Table 2 and Table 3. I then compare the Sharpe ratio of each of the trading rules against their
respective buy-and-hold as shown in Table 4.
Table 2
The number of countries outperforming and underperforming their respective buy-and-hold strategies in both in-sample
(1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods
before accounting for possible trading costs of the strategy. The percentage shown in brackets tells the relative size
compared to the total size of the pool of countries in emerging markets and developed markets, respectively.
Outperform buy-and-hold Underperform buy-and-hold
Emerging market Developed market Emerging market Developed market
In Out In Out In Out In Out
RSI 15 (88%) 9 (53%) 10 (53%) 11 (58%) 2 (12%) 8 (47%) 9 (47%) 8 (42%)
TRB50 12 (71%) 11 (65%) 8 (42%) 7 (37%) 5 (29%) 6 (35%) 11 (58%) 12 (63%)
TRB150 12 (71%) 7 (41%) 4 (21%) 8 (42%) 5 (29%) 10 (59%) 15 (79%) 11 (58%)
TRB200 8 (47%) 9 (53%) 10 (53%) 8 (42%) 9 (53%) 8 (47%) 9 (47%) 11 (58%)
MACD 15 (88%) 14 (82%) 9 (47%) 11 (58%) 2 (12%) 3 (18%) 10 (53%) 8 (42%)
STOCH-D 12 (71%) 8 (47%) 7 (37%) 7 (37%) 5 (29%) 9 (53%) 12 (63%) 12 (63%)
OBV 10 (59%) 9 (53%) 6 (32%) 8 (42%) 7 (41%) 8 (47%) 13 (68%) 11 (58%)
Kelly
criterion - 8 (47%) - 12 (63%) - 9 (53%) - 7 (37%)
20
Table 3
Average buy-and-hold index (section 3.2.1.2) in emerging market and developed market in both in-sample (1st of
September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods. Numbers
in brackets are the median of the buy-and-hold index. The buy-and-hold index is the performance of the trading rule
divided by the performance of the buy-and-hold strategy.
Average (Median) buy-and-hold index
Emerging market Developed market
In Out In Out
RSI 41,5% (35,0%) 15,6% (4,2%) 7,42% (0,52%) 4,14% (0,94%)
TRB50 37,2% (19,7%) 6,8% (10,3%) -4,79% (-6,44%) -2,57% (-3,65%)
TRB150 24,8% (6,9%) 0,9% (-2,8%) -2,57% (-5,94%) -2,13% (-3,58%)
TRB200 24,3% (-0,9%) 4,3% (1,4%) 1,33% (0,13%) -2,62% (6,71%)
MACD 51,7% (17,8%) 34,2% (20,5%) 1,05% (-9,15%) 9,79% (7,81%)
STOCH-D 57,1% (19,5%) 6,1% (-3,1%) 1,08% (-4,74%) -1.61% (-2,44%)
OBV 41,6% (17,2%) 13,9% (11,6%) -9,28% (-14,71%) -6,52% (-9,1%)
Kelly criterion - 122,5% (66,5%) - 43,71% (34,38%)
Table 4
The number of countries outperforming and underperforming their respective buy-and-hold indices in both in-sample (1st
of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods in
terms of Sharpe ratio. The percentage tells the relative size compared to the total size of the pool of countries in emerging
markets and developed markets, respectively.
Outperform buy-and-hold Sharpe ratio Underperform buy-and-hold Sharpe ratio
Emerging market Developed market Emerging market Developed market
In Out In Out In Out In Out
RSI 15 (88%) 11 (65%) 12 (63%) 12 (63%) 2 (12%) 6 (35%) 7 (37%) 7 (37%)
TRB50 10 (59%) 8 (47%) 9 (47%) 10 (53%) 7 (41%) 9 (53%) 10 (53%) 9 (47%)
TRB150 6 (35%) 5 (29%) 4 (21%) 6 (32%) 11 (65%) 12 (71%) 15 (79%) 13 (68%)
TRB200 4 (24%) 5 (29%) 8 (42%) 4 (21%) 13 (76%) 12 (71%) 11 (58%) 15 (79%)
MACD 13 (76%) 12 (71%) 8 (42%) 12 (63%) 4 (24%) 5 (29%) 11 (58%) 7 (37%)
STOCH-D 11 (65%) 5 (29%) 8 (42%) 7 (37%) 6 (35%) 12 (71%) 11 (58%) 12 (63%)
OBV 9 (53%) 7 (41%) 8 (42%) 6 (32%) 8 (47%) 10 (59%) 11 (58%) 13 (68%)
Kelly
criterion - 9 (53%) - 12 (63%) - 8 (47%) - 7 (37%)
Sharpe ratio is calculated as described by Sharpe (1994), that is by using the following equation 𝑆𝑎 =𝑅𝑝−𝑅𝑓
𝜎𝑝 in which 𝑆𝑎 is the
Sharpe ratio of the asset, 𝑅𝑝 is the return of the asset, 𝑅𝑓 is the risk-free rate and 𝜎𝑝 stands for the standard deviation of the
excess return over the risk-free rate.
As we can see from Tables 2, 3, and 4 every single trading rule with exception of
TRB200 performs better in-sample than out-of-sample in emerging market economies,
compared to the simple buy-and-hold strategy. The same kind of general observation cannot be
21
made for the developed market economies. Table 3 shows the average buy-and-hold index in-
sample period in emerging markets painting a clear picture of technical indicators beating the
buy-and-hold strategy. This performance seems to significantly reduce in an out-of-sample
period and arguably only RSI, TRB50, MACD, and OBV clearly beat the buy-and-hold strategy
as an aggregate. This result implies the emerging markets have become more efficient over time
and technical indicators do not perform as well in the present as they did in the past. This
implication is opposite to what Griffin, Kelly, and Nardari (2010) find in their study but seems
to be supported by the findings of Hull and McGroarty (2014) and Bekaert and Harvey (2002).
Even though the number of countries in which Kelly criterion optimized portfolio
manages to beat buy-and-hold strategy in the emerging market is much lower than in most of
the individual technical trading rules, the absolute aggregate outperformance compared to buy-
and-hold strategy is economically significant. In Table 3 the average and median buy-and-hold
index are poor in the individual technical indicators in developed markets in both in-sample and
out-of-sample periods but implementing the Kelly criterion manages to significantly improve
both average and median buy-and-hold index.
These results suggest technical trading rules outperform their respective simple buy-
and-hold strategies in the emerging market more than in developed markets, especially RSI and
MACD perform much better in emerging markets. However, the Kelly criterion performs
relatively better in developed economies than in the emerging economies as an aggregate,
though the relative difference here is much closer than in the technical trading rules used
separately. It does not seem that implementing the Kelly criterion on the technical trading rules
in emerging markets generates risk-adjusted profits, but in the category of developed markets,
in which the technical trading rules do not individually perform as well as they do on the
emerging market, the inclusion of the Kelly criterion improve both the absolute performance as
well as the risk-adjusted performance of the strategy.
22
Table 5
The average percentage of profitable trades in each trading rule in both in-sample (1st of September 2011 to 31st of
December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods. The number in brackets is the
median of the percentage of profitable trades.
Average (median) percentage of profitable trades
Emerging market Developed market
In Out In Out
RSI 53,5% (50,0%) 59,5% (57,1%) 69,8% (66,7%) 62,9% (60,0%)
TRB50 38,9% (33,3%) 39,2% (37,5%) 45,6% (44,4%) 46,6% (44,4%)
TRB150 22,5% (0,0%) 39,2% (50,0%) 38,2% (50,0%) 52,6% (50,0%)
TRB200 23,5% (0,0%) 47,1% (50,0%) 65,8% (100,0%) 57,9% (50,0%)
MACD 36,6% (35,6%) 38,9% (38,6%) 38,8% (39,5%) 41,4% (41,2%)
STOCH-D 36,2% (36,9%) 38,5% (39,4%) 37,8% (37,8%) 41,2% (40,9%)
OBV 33,1% (33,6%) 36,3% (35,0%) 35,2% (35,9%) 37,3% (37,0%)
Kelly criterion - 39,8% (39,9%) - 40,9% (39,7%)
The outperformance of the technical trading rules and Kelly criterion compared to the
simple buy-and-hold strategy does not come from the technical trading rules ability to predict
future market movement direction, because on average every technical trading rule, with a
minor exception of RSI, has less than fifty percent of profitable trades as shown in Table 5. The
TRB200 rule in developed markets has above fifty percent as well, but due to the trading rule
making very few trades in either period this result is not very informative. Therefore, profitable
technical trading rules must be profitable only because they have a positive asymmetry between
an average winning trade and average losing trades, which means these technical trading rules
provide a utility to individual investors who suffer from the disposition effect as described by
Odean (1998). This result is the same Tharavanij, Siraprapasiri, and Rajchamaha (2015) also
find with similar technical indicators.
23
Table 6
Median breakeven cost for profitable trades in each trading rule. The technical trading rules data is from the full period (1 st
of September 2011 to 19th of June 2020) and Kelly criterion is from out-of-sample (4th of January 2016 to 19th of June
2020). The number in brackets is the number count of profitable trades.
Median breakeven cost
Emerging market Developed market
RSI 5,32% (9) 2,98% (16)
TRB50 3,10% (6) 2,53% (8)
TRB150 1,19% (3) 4,25% (10)
TRB200 1,98% (2) 6,11% (10)
MACD 0,60% (11) 0,25% (15)
STOCH-D 0,16% (6) 0,06% (9)
OBV 0,13% (7) 0,03% (8)
Kelly criterion 0,95% (8) 0,39% (12)
Breakeven costs are calculated as described by Bessembinder and Chan (1998) equation 𝐶 =𝜋
𝑠𝑏𝑢𝑦 in which 𝑠𝑏𝑢𝑦 is the number
of the buy signal generated by the trading rule in total and 𝜋 is the additional return generated by the trading rules compared
to the simple buy-and-hold strategy, that is 𝜋 = ∑ 𝑟𝑖𝑛𝑏𝑢𝑦
𝑖=1 in which 𝑛𝑏𝑢𝑦 is the number of days the long position is being held,
𝑟𝑖 is the return of the long position on the specific day i.
For the profitable trading rules to also work in practice they must achieve a reasonable
breakeven cost. What exactly is reasonable breakeven costs is highly dependent on the type of
trader, as institutional investors often enjoy much lower trading costs than retail investors do.
The way to interpret Table 6 is that if one has higher trading costs than the breakeven cost, the
investor will not be able to achieve higher profits and the performance of the technical trading
rule is purely hypothetical. The reason why here I have used median breakeven cost is simply
that breakeven cost can only be calculated for profitable trading rules, meaning it is always a
positive number and thus individual outliers in data tends to drag the average higher, and thus
median is more realistic measurement as a simple mean is almost always higher than the
median.
Only because a trading rule has a high breakeven cost, like TRB150 and TRB200, it
does not mean that those trading rules beat the simple buy-and-hold strategy, as the trading
rules can achieve high breakeven cost by very infrequent trading, thus losing in most cases to
any simple buy-and-hold strategies having positive returns in the test periods. This
measurement is better thought of as just a filter to filter out the trading rules that trade very
frequently but fail to achieve economically significant results by doing so. The trading rules
most likely failing to meet this qualification are unsurprisingly MACD, STOCH-D, and OBV
as these are the trading rules generating trade signals by far the most frequently.
24
By evaluating the performance of the technical trading rules based on Tables 2-6 the
best performing rules are RSI and TRB50 and these two trading rules seem to outperform the
Kelly criterion optimized portfolios in aggregate based on the aggregate values shown in these
Tables.
4.2. Statistic test and Fama-French 5-factor regression
Next, I perform the statistical significance test for the performance generated resulting
in a Z-value for each induvial trading rule.
Table 7
The number of country ETFs having a critical Z-value. The percentage indicates the portion of statistically significant
results, at 10%, 5%, 1% significance level, compared to the whole pool of emerging market or developed market
economies, respectively. The data is from 1st of September 2011 to 19th of June 2020 in individual trading rules and 4th of
January 2016 to 19th of June 2020 for Kelly criterion.
Number of critical Z-values
Emerging market Developed market
RSI 2 (12%) -
TRB50 1 (6%) 3 (16%)
TRB150 - 1 (5%)
TRB200 - 1 (5%)
MACD 5 (29%) -
STOCH-D 3 (18%) -
OBV 2 (12%) -
Kelly criterion 3 (18%) 2 (11%)
The Z-values are calculated as described by Tharavanij et al. (2015). That is by using the equation 𝑍𝑏𝑢𝑦 =�̅�𝑏𝑢𝑦
(𝑆𝑏𝑢𝑦
√𝑛𝑏𝑢𝑦), 𝑆𝑏𝑢𝑦 =
√∑ (𝑟𝑖−�̅�𝑏𝑢𝑦)2
𝑖∈𝜙𝑏𝑢𝑦
(𝑛𝑏𝑢𝑦−1) in which �̅�~𝑁 (𝜇,
𝜎2
𝑛), 𝜙 is the union of all disjoint intervals generated by the buy signals, 𝑛𝑏𝑢𝑦 is the number
of days the long position is held and �̅� =∑ 𝑟𝑖𝑖∈𝜙
𝑛, in which 𝑟𝑡 = ln (
𝑃𝑡
𝑃𝑡−1) that is continuously compounding daily returns from
the closing prices.
Overall, the empirical results in this study support the weak form of the efficient market
hypothesis as described by Fama (1970), because as we can observe from Table 7 technical
trading rules overall do not provide many statistically significant results in this data set. Even
though overall the technical trading rules do not provide statistically significant results, the few
significant results concentrate heavily on the side of emerging market economies, rather than
in the developed markets, but the few significant results in the Kelly criterion are more evenly
distributed between the two market types.
25
Although one cannot draw strong conclusions from such a small amount of statistically
significant results, the results suggest TRB trading rules are somewhat more effective in the
developed market, but TRB150 and TRB200 trading rules are not good in general. Other
technical trading rules do not result in any kind of statistically significant results in developed
markets but provide some statistically significant results in emerging markets individually. This
result also means for every single trading rule, except the TRB ones, the hypothesis of emerging
markets being less efficient than the developed markets, does hold as an aggregate. However,
the Kelly criterion does not confirm these results and the null hypothesis stands. This result is
also consistent with the implication from Hsu, Hsu, Kuan (2010) paper in which they argue that
the inception of ETFs was a convenient tool for arbitrageurs to reap the rewards of the market
inefficiencies that existed in emerging market economies prior to the inception of ETFs but
have been since largely arbitraged away.
Afterward, I perform a Fama-French 5-factor regression to determine which strategies
manage to generate alpha. The country-specific information about the 5-factor regression can
be found in Appendix 78 for individual trading rules and Appendix 73 for the Kelly criterion.
Table 8
The number of statistically significant, at 10%, 5%, 1% level, alpha in Fama-French 5-factor regression, as described by
Fama and French (2015). The percentage indicates the portion of significant results compared to all the countries in the
emerging market or developed market category. Emerging market data is used for emerging markets and developed ex-US
data is used for developed market regressions. The data is from 1st of September 2011 to 19th of June 2020 in individual
trading rules and 4th of January 2016 to 19th of June 2020 for Kelly criterion and all Fama-French 5-factor data is on
monthly basis.
Number of significant positive alpha Number of significant negative alpha
Emerging market Developed market Emerging market Developed market
RSI - - 2 (12%) 8 (42%)
TRB50 - 1 (5%) 4 (24%) -
TRB150 - - 2 (12%) 1 (5%)
TRB200 - 1 (5%) 3 (18%) -
MACD 2 (12%) - 1 (6%) -
STOCH-D 1 (6%) - 3 (18%) 2 (11%)
OBV 1 (6%) - 5 (29%) 4 (21%)
Kelly criterion 2 (12%) - 3 (18%) 2 (11%)
Only in couple emerging market economies Kelly criterion manages to generate
statistically significant positive alpha in Fama-French 5-factor regression but does not manage
to do so in the developed market, as shown in Table 8. Only two countries Kelly criterion
generates statistically significant positive alpha in total, whereas in three emerging market
26
countries and two developed market countries generate negative statistically significant alpha.
Overall, the amount of statistically significant alphas, both positive and negative, generated by
the Kelly criterion are so low that there are no strong conclusions that can be drawn from this
data set.
In individual trading rules, in which very few individual trading rules generate
statistically significant positive alpha, but quite a few individual trading rules, especially in
developed market economies the RSI indicator, generate statistically significant negative alpha.
Once again, the overall number of statistically significant results here is not large enough to
draw strong conclusions, but it is quite clear none of the individual trading rules, with arguably
the exception of MACD in the case of emerging market economies, generate statistically
significant positive alpha and are more likely to generate negative statistically significant alpha.
The most likely reason behind the lack of statistically significant positive alpha, in the
emerging market specifically in which trading rules does manage to outperform simple buy-
and-hold, is the fact that most emerging market economies chosen to this study have negative
returns from buy-and-hold and the emerging market factor from Kenneth French data library
has positive factors, as it accounts for the whole emerging market. The same kind of difference
exists in developed market ex-US factors as well as it represents the developed market as a
whole and in this study, there is only a limited number of chosen countries. These mixed results
are also partially attributed to taking the viewpoint of a US-based investor and taking USD
returns on each country ETF and thus every portfolio is also exposed to currency risk as
appreciations and depreciation of these different currencies compared to USD affects the
performance significantly, and this risk is not reflected in the Fama-French 5-factors.
Like Yu et al.'s (2013) research the moving average technical trading rule manages to
generate statistically significant positive alpha in Thailand. Tharavanij, Siraprapasiri, and
Rajchamaha (2015) also find specifically in Thailand technical trading rules manages to
outperform simple buy-and-hold strategies. These inefficiencies in markets have seemingly
persistent even after they had been published in these papers, as the data used in this study is
the most recent out of all the studies mentioned in the paper and covers a significant time period
after these papers were published.
Lastly, I take a single technical indicator and combining all the different countries' ETFs
using an individual indicator and using equation 14 to get the Kelly criterion for each of the
technical indicators. The portfolio weights of each of these combined portfolios can be found
in Appendix 75. After combining the different countries, I divide the countries into emerging
markets and developed market economies and take a single technical indicator to generate Kelly
27
criterion portfolios for both categories of countries. These specific portfolio weights can be
found in Appendix 76 and 77, respectively. For all three categories of portfolios, I perform the
Fama-French 5-factor regression as individual trading rules and got the results as shown in
Table 9 below.
Table 9
Fama-French 5-factor model regression, as described by Fama and French (2015), for a single technical indicator in all
36 countries using Kelly criterion to determine portfolio weights. Combined using US data, developed countries using
developed excluding US data and emerging countries using emerging market data for regression. All data is in a monthly
format. – means that at one point or another the strategy lost everything due to high leverage. ***, **, * represent
statistical significance at 1%, 5%,10% level, respectively. The data is from the 4th of January 2016 to the 19th of June
2020.
a b s h r c
Developed MACD -1,69 % 5,68*** -6,71 2,32 2,98 -7,92
Developed TRB50 - - - - - -
Developed TRB150 -2,43 % 4,19*** -1,01 0,11 4,94 5,00*
Developed TRB200 - - - - - -
Developed RSI - - - - - -
Developed STOCH-D - - - - - -
Developed OBV - - - - - -
Emerging MACD 1,76 % 2,90*** -2,86 -3,63* -2,98 3,37
Emerging TRB50 -1,95 % 2,95*** -3,46** -3,74*** 1,35 5,74**
Emerging TRB150 - - - - - -
Emerging TRB200 - - - - - -
Emerging RSI - - - - - -
Emerging STOCH-D - - - - - -
Emerging OBV 1,23 % 2,55*** -1,86 -0,66 -3,16* 0,29
Combined MACD 0,15 % 6,21*** -5,16* -0,25 -4,38 2,40
Combined TRB50 -1,43 % 3,46** 0,04 -2,36 -0,68 2,65
Combined TRB150 - - - - - -
Combined TRB200 - - - - - -
Combined RSI - - - - - -
Combined STOCH-D - - - - - -
Combined OBV - - - - - -
From Table 9 one can easily see most of the strategies using multiple countries went
bankrupt at one point or another due to the high level of leverage suggested by Kelly criterion.
Only MACD manages to survive the whole testing period in all three categories. The second
observation is that none of the strategies in any of the categories generate a statistically
significant amount of alpha. The fact that most of the strategies lost everything and roughly half
28
of the ones that do not lose everything generate negative alpha, even though it is statistically
insignificant in every scenario, does not bode well for this strategy.
Table 9 is in which one can see most clearly the shortcomings of this study. First, the
choice of trading in ETFs which is the justification of only including the long trades and
ignoring the short signals is partially the reason why Kelly criterion optimized portfolios are
overleveraged in the study, as they lack the crucial short leg of the trade always resulting in the
higher net percentage of asset allocation. Secondly, the relatively short in-sample period results
in overly optimistic mean daily returns for the strategies that in turn act as an amplifier in the
Kelly criterion formula resulting in high leverage in certain trading rules. This result can be best
seen in this combination test in which the Kelly criterion has an increased amount of possible
assets to choose from and it favors the best performing trading rules, which are simultaneously
most likely overestimating their performance. This analysis is the same as Thorp (2006) shows
that the “overbetting” the optimal ratio, which is in every case unknown ex-ante, results in a
higher loss than “underbetting” would and the estimates for the mean return are quite likely too
high.
Before finishing the statistical analysis and multifactor regression it looked like there
might have been a good case made for individual technical indicators outperforming their
respective buy-and-hold strategies especially in emerging market economies and implementing
the Kelly criterion with multiple technical indicators would result in better performance in both
absolute and risk-adjusted terms especially in developed markets. However, after going through
the more throughout analysis of the statistical significance most of the results disappear,
especially in developed markets, whereas emerging markets seem to still have some statistically
significant results generated by the individual technical trading rules. Once implementing the
Fama-French 5-factor regression almost all the results are explainable with weightings in the
5-factors and almost none of the trading rules generate statistically significant positive alpha
and are more likely to generate statistically significant negative alpha instead.
The only countries managing to generate very significant alpha, in both statistical and
economical meaning of the word, using Kelly criterion are Peru, which manages to generate
4,32 % monthly alpha at 1,14 % breakeven cost per trade, while significantly outperforming
the buy-and-hold strategies Sharpe ratio (1,95 to 0,31) and Thailand, which generates 2,12 %
monthly alpha at 2,32 % breakeven cost per trade and Sharpe ratio of 0,76, while the simple
buy-and-hold strategy has Sharpe ratio of 0,16. These statistical outliers alone are not surprising
considering the total amount of countries included in this data set. After combining all 36
29
country-specific ETFs and further dividing them into developed markets and emerging markets
the Kelly criterion does not generate statistically significant alpha in any of these scenarios.
The fact that technical indicators and Kelly criterion manage to create a statistically and
economically significant alpha in Thailand in this paper in a time after Yu et al. (2013),
Gunasekarage and Power (2001), and Tharavanij, Siraprapasiri, and Rajchamaha (2015) show
in their respective papers that the use of technical trading rules results in excess profits
specifically in Thailand is particularly interesting as logically the excess profits should have
been arbitraged away since the discovery of the existence these excess returns.
30
5. Summary and conclusions
This paper studies the profitability of five different categories of technical trading rules:
Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Trading
Range Breakout (TRB), Stochastic Oscillator D-variation (STOCH-D), and On Balance
Volume (OBV). Also, the paper implements a money management aspect to the technical
trading rules by using the Kelly criterion. These trading rules are then tested in 36 different
country-specific ETFs that are further categorized into emerging market economies and
developed market economies. The data in the paper is split into two periods: the in-sample
period from 1st of September 2011 to 31st of December 2015 and the out-of-sample period from
4th of January 2016 to 19th of June 2020. These results are then compared against a simple buy-
and-hold strategy.
Overall, the empirical results in this paper show to support the weak form of market
efficiency as described by Fama (1970) in most of the 36 selected countries with few statistical
outliers being Peru and Thailand, as most of the trading rules does not manage to generate a
statistically significant result or do not manage to generate a statistically significant positive
alpha in Fama-French 5-factor regression. The individual technical trading rules perform much
better in emerging market economies as an aggregate compared to developed market
economies, but the implementation of money management by Kelly criterion improves both the
risk-adjusted and absolute performance of the technical trading rules in developed economies
to seemingly match their counterparts in emerging markets. Combining the countries to include
multiple choices of ETFs per single technical trading rule for Kelly criterion does not boost
performance and results in most of the individual technical trading rules losing everything
during the test period, due to high leverage. None of the combined portfolios generates
statistically significant results.
The usefulness of the trading rules and the relatively good performance is mostly
attributable to allowing the winning trades to run long and cut the losing trades early, thus
helping individual traders in practice to account for their own behavioral bias called disposition
effect (Odean 1998). This result means that even if technical trading rules do not manage to
generate statistically significant results violating the weak form of market efficiency, they could
still in practice act as a useful auxiliary tool for traders to keep their behavioral biases in check.
One can also observe that even the most profitable trading rules, with exception of RSI, could
not predict subsequent market movements directions as they all had less than 50% of profitable
trades out of all their trades, thus showing that technical indicators hold no predictive power
31
over the market’s future developments. Even the one outlier RSI does not manage to predict
the direction of the movements consistently enough that one could make a solid argument in
favor of this one technical trading rule, though it does manage to perform better than a coin
toss.
The limitations of this study are the relatively short period of time for in- and out-of-sample
periods individually, as a total length of roughly 10 years is quite extensive but splitting this
further into two sub-periods to get Kelly criterion parameters results in a relatively short period
that is more prone to overestimating the true longer-term performance. Other limitations are the
long-only portfolio due to the choice of instrument and the performance affected by the
exposure to currency risk, which is not being reflected in the comparable factors in Fama-
French 5-factor regression, resulting in a lack of statistically significant results in either
direction.
Future studies could implement a significantly longer in-sample period to acquire more
accurate data for the money management aspect of Kelly criterion as in this study some
technical trading rules do not manage to make many trades in the in-sample period or do not
for example make a single losing (winning) trade because of the small number of total trades.
Secondly, future studies should implement long-short portfolios to see how well the Kelly
criterion performs as this version of Kelly criterion does require it to work as intended and this
study is limited on only having the long leg of the trade. Thirdly an interesting future research
focus could be more specifically in the two outliers Peru and Thailand and try to replicate this
level of significant alpha in a different data set to see if they persist or if they are only achievable
in this specific time frame as a statistical outlier.
32
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36
Appendix
Appendix 1: Statistics for Taiwan ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Taiwan RSI TRB50 TRB150
Average daily return of a strategy 0,06 % -0,01 % -0,01%
Standard deviation of daily return 1,43 % 1,02 % 1,06%
Z statistic 1,491* -0,359 -0,177
Breakeven trading cost 6,71 % Unprofitable Unprofitable
Number of signals generated 11 16 5
TRB200 MACD STOCH-D
Average daily return of a strategy 0,01% 0,05 % 0,01 %
Standard deviation of daily return 1,06% 1,13 % 1,28 %
Z statistic 0,165 1,395* 0,228
Breakeven trading cost 2,15 % 0,60 % 0,03 %
Number of signals generated 3 86 356
OBV Kelly criterion
Average daily return of a strategy -0,02 % 0,10 %
Standard deviation of daily return 1,23 % 2,56 %
Z statistic -0,595 1,293*
Breakeven trading cost Unprofitable 2,13 %
Number of signals generated 468 50
37
Appendix 2: Statistics for China ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
China RSI TRB50 TRB150
Average daily return of a strategy 0,06 % 0,02 % -0,01%
Standard deviation of daily return 1,65 % 1,20 % 1,30%
Z statistic 1,185 0,636 -0,176
Breakeven trading cost 6,55 % 1,90 % Unprofitable
Number of signals generated 10 14 5
TRB200 MACD STOCH-D
Average daily return of a strategy -0,02% 0,02 % -0,01 %
Standard deviation of daily return 1,30% 1,35 % 1,46 %
Z statistic -0,490 0,468 -0,258
Breakeven trading cost Unprofitable 0,23 % Unprofitable
Number of signals generated 4 91 369
OBV Kelly criterion
Average daily return of a strategy 0,04 % 0,08 %
Standard deviation of daily return 1,41 % 3,07 %
Z statistic 1,040 0,856
Breakeven trading cost 0,13 % 1,53 %
Number of signals generated 391 57
38
Appendix 3: Statistics for New Zealand ETF. ***, **, * represent statistical significance at 1%, 5%,10% level,
respectively. Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and
out-of-sample (4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
New Zealand RSI TRB50 TRB150
Average daily return of a strategy 0,02 % 0,04 % 0,00%
Standard deviation of daily return 1,59 % 0,96 % 0,99%
Z statistic 0,413 1,647** 0,147
Breakeven trading cost 3,11 % 4,45 % 0,90 %
Number of signals generated 6 13 6
TRB200 MACD STOCH-D
Average daily return of a strategy 0,02% 0,03 % -0,02 %
Standard deviation of daily return 0,99% 1,12 % 1,33 %
Z statistic 0,809 0,794 -0,434
Breakeven trading cost 10,30 % 0,32 % Unprofitable
Number of signals generated 3 93 375
OBV Kelly criterion
Average daily return of a strategy -0,01 % 0,11 %
Standard deviation of daily return 1,12 % 2,67 %
Z statistic -0,430 1,408*
Breakeven trading cost Unprofitable 0,50 %
Number of signals generated 386 250
39
Appendix 4: Statistics for Netherland ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Netherland RSI TRB50 TRB150
Average daily return of a strategy 0,04 % 0,01 % 0,02%
Standard deviation of daily return 1,49 % 1,06 % 0,99%
Z statistic 0,743 0,222 0,665
Breakeven trading cost 4,62 % 0,54 % 6,12 %
Number of signals generated 7 16 4
TRB200 MACD STOCH-D
Average daily return of a strategy 0,01% 0,04 % 0,02 %
Standard deviation of daily return 0,99% 1,09 % 1,30 %
Z statistic 0,368 1,163 0,475
Breakeven trading cost 3,23 % 0,48 % 0,06 %
Number of signals generated 4 89 358
OBV Kelly criterion
Average daily return of a strategy 0,02 % 0,08 %
Standard deviation of daily return 1,23 % 7,65 %
Z statistic 0,488 0,334
Breakeven trading cost 0,05 % 1,60 %
Number of signals generated 387 52
40
Appendix 5: Statistics for Switzerland ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Switzerland RSI TRB50 TRB150
Average daily return of a strategy 0,03 % 0,03 % 0,02%
Standard deviation of daily return 1,26 % 0,79 % 0,85%
Z statistic 0,698 1,334* 0,863
Breakeven trading cost 2,82 % 2,92 % 6,54 %
Number of signals generated 9 13 4
TRB200 MACD STOCH-D
Average daily return of a strategy 0,01% 0,02 % 0,03 %
Standard deviation of daily return 0,85% 0,89 % 1,02 %
Z statistic 0,553 0,734 0,991
Breakeven trading cost 3,94 % 0,23 % 0,10 %
Number of signals generated 4 95 356
OBV Kelly criterion
Average daily return of a strategy 0,00 % 0,05 %
Standard deviation of daily return 1,00 % 5,35 %
Z statistic -0,093 0,292
Breakeven trading cost Unprofitable 0,26 %
Number of signals generated 439 191
41
Appendix 6: Statistics for Japan ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Japan RSI TRB50 TRB150
Average daily return of a strategy 0,04 % -0,01 % -0,01%
Standard deviation of daily return 1,26 % 0,90 % 0,95%
Z statistic 0,952 -0,255 -0,273
Breakeven trading cost 5,01 % Unprofitable Unprofitable
Number of signals generated 8 18 6
TRB200 MACD STOCH-D
Average daily return of a strategy 0,01% 0,02 % -0,01 %
Standard deviation of daily return 0,95% 1,01 % 1,06 %
Z statistic 0,398 0,666 -0,457
Breakeven trading cost 3,31 % 0,25 % Unprofitable
Number of signals generated 4 90 372
OBV Kelly criterion
Average daily return of a strategy 0,01 % 0,07 %
Standard deviation of daily return 0,99 % 5,09 %
Z statistic 0,271 0,459
Breakeven trading cost 0,02 % 0,27 %
Number of signals generated 454 284
42
Appendix 7: Statistics for Sweden ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Sweden RSI TRB50 TRB150
Average daily return of a strategy 0,04 % -0,01 % -0,01%
Standard deviation of daily return 1,59 % 1,30 % 1,17%
Z statistic 0,867 -0,349 -0,291
Breakeven trading cost 5,05 % Unprofitable Unprofitable
Number of signals generated 9 15 4
TRB200 MACD STOCH-D
Average daily return of a strategy -0,03% -0,02 % -0,01 %
Standard deviation of daily return 1,17% 1,38 % 1,49 %
Z statistic -0,621 -0,381 -0,305
Breakeven trading cost Unprofitable Unprofitable Unprofitable
Number of signals generated 4 89 356
OBV Kelly criterion
Average daily return of a strategy -0,01 % -0,01 %
Standard deviation of daily return 1,45 % 6,29 %
Z statistic -0,163 -0,032
Breakeven trading cost Unprofitable Unprofitable
Number of signals generated 420 232
43
Appendix 8: Statistics for Israel ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Israel RSI TRB50 TRB150
Average daily return of a strategy 0,06 % -0,02 % -0,03%
Standard deviation of daily return 1,45 % 0,99 % 0,93%
Z statistic 1,145 -0,613 -0,880
Breakeven trading cost 4,26 % Unprofitable Unprofitable
Number of signals generated 11 16 6
TRB200 MACD STOCH-D
Average daily return of a strategy -0,02% 0,03 % -0,01 %
Standard deviation of daily return 0,93% 1,00 % 1,17 %
Z statistic -0,557 0,976 -0,263
Breakeven trading cost Unprofitable 0,38 % Unprofitable
Number of signals generated 4 90 358
OBV Kelly criterion
Average daily return of a strategy -0,04 % 0,06 %
Standard deviation of daily return 1,15 % 5,00 %
Z statistic -1,221 0,400
Breakeven trading cost Unprofitable 0,27 %
Number of signals generated 358 237
44
Appendix 9: Statistics for Germany ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Germany RSI TRB50 TRB150
Average daily return of a strategy 0,01 % 0,02 % 0,00%
Standard deviation of daily return 1,67 % 1,16 % 1,14%
Z statistic 0,207 0,690 0,094
Breakeven trading cost 1,42 % 2,12 % 0,91 %
Number of signals generated 7 13 4
TRB200 MACD STOCH-D
Average daily return of a strategy 0,01% -0,01 % 0,01 %
Standard deviation of daily return 1,14% 1,33 % 1,42 %
Z statistic 0,358 -0,160 0,267
Breakeven trading cost 3,54 % Unprofitable 0,04 %
Number of signals generated 3 90 345
OBV Kelly criterion
Average daily return of a strategy 0,00 % 0,00 %
Standard deviation of daily return 1,42 % 9,58 %
Z statistic 0,086 -0,014
Breakeven trading cost 0,01 % Unprofitable
Number of signals generated 430 237
45
Appendix 10: Statistics for South Korea ETF. ***, **, * represent statistical significance at 1%, 5%,10% level,
respectively. Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and
out-of-sample (4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
South Korea RSI TRB50 TRB150
Average daily return of a strategy 0,04 % 0,02 % -0,02%
Standard deviation of daily return 1,76 % 1,25 % 1,27%
Z statistic 0,832 0,406 -0,622
Breakeven trading cost 5,32 % 1,32 % Unprofitable
Number of signals generated 9 13 4
TRB200 MACD STOCH-D
Average daily return of a strategy -0,01% 0,00 % -0,02 %
Standard deviation of daily return 1,27% 1,35 % 1,52 %
Z statistic -0,234 0,063 -0,400
Breakeven trading cost Unprofitable 0,03 % Unprofitable
Number of signals generated 3 90 362
OBV Kelly criterion
Average daily return of a strategy -0,03 % Bankruptcy
Standard deviation of daily return 1,38 % Bankruptcy
Z statistic -0,808 Bankruptcy
Breakeven trading cost Unprofitable Bankruptcy
Number of signals generated 475 199
46
Appendix 11: Statistics for Belgium ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Belgium RSI TRB50 TRB150
Average daily return of a strategy 0,03 % 0,02 % 0,02%
Standard deviation of daily return 1,38 % 0,97 % 0,94%
Z statistic 0,588 0,924 0,681
Breakeven trading cost 3,62 % 2,14 % 5,58 %
Number of signals generated 7 15 4
TRB200 MACD STOCH-D
Average daily return of a strategy 0,01% 0,01 % 0,03 %
Standard deviation of daily return 0,94% 1,07 % 1,22 %
Z statistic 0,537 0,245 0,757
Breakeven trading cost 4,51 % 0,10 % 0,09 %
Number of signals generated 4 87 356
OBV Kelly criterion
Average daily return of a strategy -0,01 % 0,00 %
Standard deviation of daily return 1,24 % 15,74 %
Z statistic -0,212 0,008
Breakeven trading cost Unprofitable 0,02 %
Number of signals generated 350 192
47
Appendix 12: Statistics for Turkey ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Turkey RSI TRB50 TRB150
Average daily return of a strategy 0,03 % -0,05 % -0,07%
Standard deviation of daily return 2,29 % 1,70 % 1,69%
Z statistic 0,474 -1,032 -1,140
Breakeven trading cost 2,82 % Unprofitable Unprofitable
Number of signals generated 13 16 5
TRB200 MACD STOCH-D
Average daily return of a strategy -0,07% -0,05 % -0,03 %
Standard deviation of daily return 1,69% 1,92 % 2,01 %
Z statistic -0,872 -0,889 -0,494
Breakeven trading cost Unprofitable Unprofitable Unprofitable
Number of signals generated 3 95 353
OBV Kelly criterion
Average daily return of a strategy -0,02 % -0,09 %
Standard deviation of daily return 1,90 % 8,31 %
Z statistic -0,394 -0,328
Breakeven trading cost Unprofitable Unprofitable
Number of signals generated 433 236
48
Appendix 13: Statistics for Ireland ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Ireland RSI TRB50 TRB150
Average daily return of a strategy 0,01 % 0,06 % 0,04%
Standard deviation of daily return 1,54 % 1,13 % 1,10%
Z statistic 0,180 2,002** 1,310*
Breakeven trading cost 1,25 % 6,91 % 12,84 %
Number of signals generated 7 12 4
TRB200 MACD STOCH-D
Average daily return of a strategy 0,03% 0,02 % 0,02 %
Standard deviation of daily return 1,10% 1,25 % 1,34 %
Z statistic 1,001 0,449 0,546
Breakeven trading cost 9,61 % 0,18 % 0,07 %
Number of signals generated 4 103 352
OBV Kelly criterion
Average daily return of a strategy 0,00 % Bankruptcy
Standard deviation of daily return 1,30 % Bankruptcy
Z statistic 0,032 Bankruptcy
Breakeven trading cost 0,00 % Bankruptcy
Number of signals generated 374 66
49
Appendix 14: Statistics for France ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
France RSI TRB50 TRB150
Average daily return of a strategy 0,00 % -0,01 % 0,01%
Standard deviation of daily return 1,60 % 1,15 % 0,99%
Z statistic -0,068 -0,172 0,371
Breakeven trading cost Unprofitable Unprofitable 2,92 %
Number of signals generated 6 16 4
TRB200 MACD STOCH-D
Average daily return of a strategy 0,02% -0,01 % 0,01 %
Standard deviation of daily return 0,99% 1,28 % 1,46 %
Z statistic 0,804 -0,348 0,325
Breakeven trading cost 7,71 % Unprofitable 0,05 %
Number of signals generated 3 93 350
OBV Kelly criterion
Average daily return of a strategy -0,01 % 0,05 %
Standard deviation of daily return 1,34 % 2,79 %
Z statistic -0,144 0,627
Breakeven trading cost Unprofitable 0,31 %
Number of signals generated 430 181
50
Appendix 15: Statistics for Canada ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Canada RSI TRB50 TRB150
Average daily return of a strategy 0,02 % 0,00 % -0,01%
Standard deviation of daily return 1,55 % 0,79 % 0,75%
Z statistic 0,337 -0,010 -0,318
Breakeven trading cost 1,73 % Unprofitable Unprofitable
Number of signals generated 10 14 4
TRB200 MACD STOCH-D
Average daily return of a strategy -0,02% 0,03 % 0,04 %
Standard deviation of daily return 0,75% 1,05 % 1,15 %
Z statistic -0,688 1,021 1,147
Breakeven trading cost Unprofitable 0,45 % 0,13 %
Number of signals generated 4 80 340
OBV Kelly criterion
Average daily return of a strategy 0,02 % 0,08 %
Standard deviation of daily return 1,08 % 2,46 %
Z statistic 0,623 1,066
Breakeven trading cost 0,06 % 0,45 %
Number of signals generated 424 186
51
Appendix 16: Statistics for Italy ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Italy RSI TRB50 TRB150
Average daily return of a strategy -0,03 % -0,04 % -0,01%
Standard deviation of daily return 1,97 % 1,47 % 1,36%
Z statistic -0,439 -0,853 -0,219
Breakeven trading cost Unprofitable Unprofitable Unprofitable
Number of signals generated 7 17 4
TRB200 MACD STOCH-D
Average daily return of a strategy 0,00% 0,03 % -0,04 %
Standard deviation of daily return 1,36% 1,64 % 1,74 %
Z statistic -0,074 0,534 -0,854
Breakeven trading cost Unprofitable 0,38 % Unprofitable
Number of signals generated 3 77 371
OBV Kelly criterion
Average daily return of a strategy -0,07 % -0,01 %
Standard deviation of daily return 1,71 % 5,54 %
Z statistic -1,367 -0,086
Breakeven trading cost Unprofitable Unprofitable
Number of signals generated 433 229
52
Appendix 17: Statistics for Malaysia ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Malaysia RSI TRB50 TRB150
Average daily return of a strategy -0,03 % -0,03 % -0,03%
Standard deviation of daily return 1,46 % 0,89 % 0,88%
Z statistic -0,946 -1,034 -0,677
Breakeven trading cost Unprofitable Unprofitable Unprofitable
Number of signals generated 7 16 3
TRB200 MACD STOCH-D
Average daily return of a strategy -0,06% -0,02 % -0,07 %
Standard deviation of daily return 0,88% 1,14 % 1,54 %
Z statistic -1,180 -0,519 -1,473
Breakeven trading cost Unprofitable Unprofitable Unprofitable
Number of signals generated 3 87 367
OBV Kelly criterion
Average daily return of a strategy -0,04 % Bankruptcy
Standard deviation of daily return 1,51 % Bankruptcy
Z statistic -0,982 Bankruptcy
Breakeven trading cost Unprofitable Bankruptcy
Number of signals generated 446 232
53
Appendix 18: Statistics for Australia ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Australia RSI TRB50 TRB150
Average daily return of a strategy 0,02 % -0,01 % -0,01%
Standard deviation of daily return 1,71 % 1,09 % 0,98%
Z statistic 0,401 -0,251 -0,412
Breakeven trading cost 3,31 % Unprofitable Unprofitable
Number of signals generated 8 15 4
TRB200 MACD STOCH-D
Average daily return of a strategy -0,03% -0,01 % 0,00 %
Standard deviation of daily return 0,98% 1,32 % 1,48 %
Z statistic -0,878 -0,296 -0,109
Breakeven trading cost Unprofitable Unprofitable Unprofitable
Number of signals generated 4 101 346
OBV Kelly criterion
Average daily return of a strategy -0,05 % -0,05 %
Standard deviation of daily return 1,44 % 6,29 %
Z statistic -1,105 -0,284
Breakeven trading cost Unprofitable Unprofitable
Number of signals generated 465 198
54
Appendix 19: Statistics for Vietnam ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Vietnam RSI TRB50 TRB150
Average daily return of a strategy 0,00 % -0,01 % -0,05%
Standard deviation of daily return 1,56 % 1,50 % 1,49%
Z statistic 0,009 -0,109 -1,180
Breakeven trading cost 0,05 % Unprofitable Unprofitable
Number of signals generated 10 15 6
TRB200 MACD STOCH-D
Average daily return of a strategy -0,05% 0,04 % -0,02 %
Standard deviation of daily return 1,49% 1,39 % 1,51 %
Z statistic -1,284 0,911 -0,471
Breakeven trading cost Unprofitable 0,49 % Unprofitable
Number of signals generated 4 89 356
OBV Kelly criterion
Average daily return of a strategy 0,00 % Bankruptcy
Standard deviation of daily return 1,47 % Bankruptcy
Z statistic -0,078 Bankruptcy
Breakeven trading cost Unprofitable Bankruptcy
Number of signals generated 426 275
55
Appendix 20: Statistics for Hong Kong ETF. ***, **, * represent statistical significance at 1%, 5%,10% level,
respectively. Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and
out-of-sample (4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Hong Kong RSI TRB50 TRB150
Average daily return of a strategy 0,05 % 0,01 % 0,00%
Standard deviation of daily return 1,49 % 0,92 % 0,96%
Z statistic 0,978 0,507 0,076
Breakeven trading cost 4,08 % 1,20 % 0,62 %
Number of signals generated 11 13 4
TRB200 MACD STOCH-D
Average daily return of a strategy 0,00% 0,04 % 0,01 %
Standard deviation of daily return 0,96% 1,06 % 1,21 %
Z statistic -0,056 1,152 0,289
Breakeven trading cost Unprofitable 0,48 % 0,03 %
Number of signals generated 3 86 362
OBV Kelly criterion
Average daily return of a strategy 0,01 % 0,05 %
Standard deviation of daily return 1,15 % 3,02 %
Z statistic 0,240 0,505
Breakeven trading cost 0,02 % 0,91 %
Number of signals generated 432 54
56
Appendix 21: Statistics for United Kingdom ETF. ***, **, * represent statistical significance at 1%, 5%,10% level,
respectively. Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and
out-of-sample (4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
United Kingdom RSI TRB50 TRB150
Average daily return of a strategy 0,02 % -0,03 % -0,01%
Standard deviation of daily return 1,38 % 1,04 % 0,83%
Z statistic 0,574 -0,977 -0,519
Breakeven trading cost 2,84 % Unprofitable Unprofitable
Number of signals generated 10 15 4
TRB200 MACD STOCH-D
Average daily return of a strategy -0,01% 0,01 % 0,00 %
Standard deviation of daily return 0,83% 1,08 % 1,24 %
Z statistic -0,224 0,327 0,130
Breakeven trading cost Unprofitable 0,15 % 0,02 %
Number of signals generated 3 83 341
OBV Kelly criterion
Average daily return of a strategy -0,01 % Bankruptcy
Standard deviation of daily return 1,22 % Bankruptcy
Z statistic -0,178 Bankruptcy
Breakeven trading cost Unprofitable Bankruptcy
Number of signals generated 438 423
57
Appendix 22: Statistics for Peru ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Peru RSI TRB50 TRB150
Average daily return of a strategy -0,03 % 0,03 % -0,01%
Standard deviation of daily return 1,45 % 1,09 % 1,10%
Z statistic -0,708 0,897 -0,302
Breakeven trading cost Unprofitable 2,64 % Unprofitable
Number of signals generated 12 12 5
TRB200 MACD STOCH-D
Average daily return of a strategy -0,02% 0,09 % 0,07 %
Standard deviation of daily return 1,10% 1,16 % 1,36 %
Z statistic -0,500 2,566*** 1,768**
Breakeven trading cost Unprofitable 1,48 % 0,23 %
Number of signals generated 3 67 347
OBV Kelly criterion
Average daily return of a strategy 0,08 % 0,31 %
Standard deviation of daily return 1,29 % 2,76 %
Z statistic 2,119** 3,212***
Breakeven trading cost 0,25 % 1,14 %
Number of signals generated 364 220
58
Appendix 23: Statistics for Norway ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Norway RSI TRB50 TRB150
Average daily return of a strategy 0,01 % -0,03 % -0,01%
Standard deviation of daily return 1,81 % 1,38 % 1,14%
Z statistic 0,116 -0,830 -0,264
Breakeven trading cost 0,85 % Unprofitable Unprofitable
Number of signals generated 8 16 4
TRB200 MACD STOCH-D
Average daily return of a strategy 0,00% 0,01 % -0,02 %
Standard deviation of daily return 1,14% 1,50 % 1,56 %
Z statistic -0,035 0,127 -0,351
Breakeven trading cost Unprofitable 0,08 % Unprofitable
Number of signals generated 3 81 362
OBV Kelly criterion
Average daily return of a strategy -0,04 % Bankruptcy
Standard deviation of daily return 1,52 % Bankruptcy
Z statistic -0,806 Bankruptcy
Breakeven trading cost Unprofitable Bankruptcy
Number of signals generated 359 193
59
Appendix 24: Statistics for Spain ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Spain RSI TRB50 TRB150
Average daily return of a strategy -0,03 % -0,01 % 0,04%
Standard deviation of daily return 1,88 % 1,32 % 1,23%
Z statistic -0,495 -0,248 0,790
Breakeven trading cost Unprofitable Unprofitable 8,75 %
Number of signals generated 7 13 3
TRB200 MACD STOCH-D
Average daily return of a strategy 0,04% 0,00 % -0,02 %
Standard deviation of daily return 1,23% 1,55 % 1,69 %
Z statistic 0,912 0,001 -0,301
Breakeven trading cost 14,83 % 0,00 % Unprofitable
Number of signals generated 2 82 354
OBV Kelly criterion
Average daily return of a strategy -0,06 % -0,04 %
Standard deviation of daily return 1,66 % 8,43 %
Z statistic -1,254 -0,133
Breakeven trading cost Unprofitable Unprofitable
Number of signals generated 437 222
60
Appendix 25: Statistics for Singapore ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Singapore RSI TRB50 TRB150
Average daily return of a strategy 0,01 % -0,03 % -0,02%
Standard deviation of daily return 1,32 % 0,86 % 0,85%
Z statistic 0,394 -1,297 -0,775
Breakeven trading cost 1,62 % Unprofitable Unprofitable
Number of signals generated 12 16 5
TRB200 MACD STOCH-D
Average daily return of a strategy -0,02% 0,05 % -0,01 %
Standard deviation of daily return 0,85% 1,08 % 1,19 %
Z statistic -0,761 1,545* -0,227
Breakeven trading cost Unprofitable 0,72 % Unprofitable
Number of signals generated 4 75 368
OBV Kelly criterion
Average daily return of a strategy 0,01 % 0,10 %
Standard deviation of daily return 1,12 % 3,78 %
Z statistic 0,353 0,844
Breakeven trading cost 0,03 % 0,43 %
Number of signals generated 433 239
61
Appendix 26: Statistics for Philippines ETF. ***, **, * represent statistical significance at 1%, 5%,10% level,
respectively. Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and
out-of-sample (4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Philippines RSI TRB50 TRB150
Average daily return of a strategy 0,00 % 0,04 % 0,00%
Standard deviation of daily return 1,91 % 1,10 % 1,16%
Z statistic -0,063 1,148 -0,031
Breakeven trading cost Unprofitable 3,56 % Unprofitable
Number of signals generated 9 12 5
TRB200 MACD STOCH-D
Average daily return of a strategy -0,02% 0,02 % 0,03 %
Standard deviation of daily return 1,16% 1,28 % 1,58 %
Z statistic -0,511 0,402 0,552
Breakeven trading cost Unprofitable 0,19 % 0,08 %
Number of signals generated 5 91 349
OBV Kelly criterion
Average daily return of a strategy 0,02 % Bankruptcy
Standard deviation of daily return 1,36 % Bankruptcy
Z statistic 0,392 Bankruptcy
Breakeven trading cost 0,04 % Bankruptcy
Number of signals generated 439 230
62
Appendix 27: Statistics for Austria ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Austria RSI TRB50 TRB150
Average daily return of a strategy 0,01 % 0,04 % 0,00%
Standard deviation of daily return 1,91 % 1,10 % 1,05%
Z statistic 0,198 1,209 0,129
Breakeven trading cost 1,28 % 4,11 % 0,59 %
Number of signals generated 9 11 5
TRB200 MACD STOCH-D
Average daily return of a strategy 0,05% 0,01 % 0,03 %
Standard deviation of daily return 1,05% 1,42 % 1,49 %
Z statistic 1,465* 0,337 0,757
Breakeven trading cost 10,78 % 0,19 % 0,11 %
Number of signals generated 2 83 344
OBV Kelly criterion
Average daily return of a strategy 0,01 % 0,13 %
Standard deviation of daily return 1,42 % 3,15 %
Z statistic 0,345 1,372*
Breakeven trading cost 0,05 % 0,60 %
Number of signals generated 350 236
63
Appendix 28: Statistics for Thailand ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Thailand RSI TRB50 TRB150
Average daily return of a strategy 0,03 % 0,04 % 0,01%
Standard deviation of daily return 1,81 % 1,07 % 1,09%
Z statistic 0,622 1,310* 0,208
Breakeven trading cost 3,50 % 4,45 % 1,99 %
Number of signals generated 11 11 4
TRB200 MACD STOCH-D
Average daily return of a strategy 0,00% 0,01 % 0,03 %
Standard deviation of daily return 1,09% 1,42 % 1,49 %
Z statistic -0,127 0,337 0,757
Breakeven trading cost Unprofitable 0,19 % 0,11 %
Number of signals generated 4 83 344
OBV Kelly criterion
Average daily return of a strategy 0,01 % 0,13 %
Standard deviation of daily return 1,42 % 3,15 %
Z statistic 0,345 1,372*
Breakeven trading cost 0,05 % 0,60 %
Number of signals generated 350 236
64
Appendix 29: Statistics for Mexico ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Mexico RSI TRB50 TRB150
Average daily return of a strategy 0,02 % -0,04 % -0,06%
Standard deviation of daily return 1,81 % 1,24 % 1,21%
Z statistic 0,421 -1,180 -1,259
Breakeven trading cost 2,49 % Unprofitable Unprofitable
Number of signals generated 11 17 4
TRB200 MACD STOCH-D
Average daily return of a strategy -0,10% 0,00 % -0,01 %
Standard deviation of daily return 1,21% 1,46 % 1,59 %
Z statistic -1,977 -0,018 -0,248
Breakeven trading cost Unprofitable Unprofitable Unprofitable
Number of signals generated 4 88 361
OBV Kelly criterion
Average daily return of a strategy -0,04 % Bankruptcy
Standard deviation of daily return 1,50 % Bankruptcy
Z statistic -0,848 Bankruptcy
Breakeven trading cost Unprofitable Bankruptcy
Number of signals generated 489 193
65
Appendix 30: Statistics for Egypt ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Egypt RSI TRB50 TRB150
Average daily return of a strategy -0,05 % 0,05 % -0,02%
Standard deviation of daily return 1,90 % 1,42 % 1,64%
Z statistic -0,943 1,088 -0,356
Breakeven trading cost Unprofitable 5,18 % Unprofitable
Number of signals generated 10 9 4
TRB200 MACD STOCH-D
Average daily return of a strategy -0,01% 0,08 % -0,08 %
Standard deviation of daily return 1,64% 1,65 % 1,68 %
Z statistic -0,217 1,680** -1,637
Breakeven trading cost Unprofitable 1,19 % Unprofitable
Number of signals generated 3 77 366
OBV Kelly criterion
Average daily return of a strategy 0,02 % Bankruptcy
Standard deviation of daily return 1,75 % Bankruptcy
Z statistic 0,383 Bankruptcy
Breakeven trading cost 0,07 % Bankruptcy
Number of signals generated 330 218
66
Appendix 31: Statistics for Indonesia ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Indonesia RSI TRB50 TRB150
Average daily return of a strategy -0,03 % -0,03 % -0,02%
Standard deviation of daily return 1,96 % 1,31 % 1,22%
Z statistic -0,611 -0,775 -0,504
Breakeven trading cost Unprofitable Unprofitable Unprofitable
Number of signals generated 5 15 4
TRB200 MACD STOCH-D
Average daily return of a strategy -0,04% -0,01 % 0,04 %
Standard deviation of daily return 1,22% 1,64 % 1,71 %
Z statistic -0,820 -0,142 0,756
Breakeven trading cost Unprofitable Unprofitable 0,12 %
Number of signals generated 3 93 349
OBV Kelly criterion
Average daily return of a strategy 0,04 % 0,04 %
Standard deviation of daily return 1,71 % 3,93 %
Z statistic 0,706 0,340
Breakeven trading cost 0,10 % 0,11 %
Number of signals generated 385 366
67
Appendix 32: Statistics for Brazil ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Brazil RSI TRB50 TRB150
Average daily return of a strategy 0,00 % -0,02 % 0,00%
Standard deviation of daily return 2,85 % 1,85 % 1,93%
Z statistic -0,043 -0,335 0,019
Breakeven trading cost Unprofitable Unprofitable 0,38 %
Number of signals generated 10 17 3
TRB200 MACD STOCH-D
Average daily return of a strategy -0,03% 0,04 % -0,12 %
Standard deviation of daily return 1,93% 2,14 % 2,35 %
Z statistic -0,412 0,685 -1,629
Breakeven trading cost Unprofitable 0,60 % Unprofitable
Number of signals generated 3 80 376
OBV Kelly criterion
Average daily return of a strategy -0,11 % Bankruptcy
Standard deviation of daily return 2,23 % Bankruptcy
Z statistic -1,601 Bankruptcy
Breakeven trading cost Unprofitable Bankruptcy
Number of signals generated 484 214
68
Appendix 33: Statistics for India ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
India RSI TRB50 TRB150
Average daily return of a strategy 0,09 % -0,05 % 0,00%
Standard deviation of daily return 1,85 % 1,45 % 1,19%
Z statistic 1,497* -1,135 0,129
Breakeven trading cost 7,74 % Unprofitable 1,19 %
Number of signals generated 11 17 4
TRB200 MACD STOCH-D
Average daily return of a strategy 0,01% 0,00 % -0,07 %
Standard deviation of daily return 1,19% 1,63 % 1,73 %
Z statistic 0,143 -0,056 -1,447
Breakeven trading cost 1,80 % Unprofitable Unprofitable
Number of signals generated 3 90 373
OBV Kelly criterion
Average daily return of a strategy -0,15 % Bankruptcy
Standard deviation of daily return 1,69 % Bankruptcy
Z statistic -3,065 Bankruptcy
Breakeven trading cost Unprofitable Bankruptcy
Number of signals generated 453 238
69
Appendix 34: Statistics for South Africa ETF. ***, **, * represent statistical significance at 1%, 5%,10% level,
respectively. Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and
out-of-sample (4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
South Africa RSI TRB50 TRB150
Average daily return of a strategy 0,05 % -0,09 % -0,05%
Standard deviation of daily return 2,30 % 1,64 % 1,61%
Z statistic 0,713 -1,649 -1,043
Breakeven trading cost 5,95 % Unprofitable Unprofitable
Number of signals generated 9 16 5
TRB200 MACD STOCH-D
Average daily return of a strategy -0,07% -0,07 % -0,03 %
Standard deviation of daily return 1,61% 1,87 % 2,02 %
Z statistic -1,179 -1,307 -0,513
Breakeven trading cost Unprofitable Unprofitable Unprofitable
Number of signals generated 4 97 361
OBV Kelly criterion
Average daily return of a strategy -0,09 % 0,04 %
Standard deviation of daily return 1,94 % 5,05 %
Z statistic -1,604 0,283
Breakeven trading cost Unprofitable 0,24 %
Number of signals generated 454 188
70
Appendix 35: Statistics for Chile ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Chile RSI TRB50 TRB150
Average daily return of a strategy -0,05 % -0,05 % -0,01%
Standard deviation of daily return 1,70 % 1,10 % 1,11%
Z statistic -1,172 -1,261 -0,263
Breakeven trading cost Unprofitable Unprofitable Unprofitable
Number of signals generated 9 15 3
TRB200 MACD STOCH-D
Average daily return of a strategy -0,01% 0,03 % 0,06 %
Standard deviation of daily return 1,11% 1,29 % 1,56 %
Z statistic -0,236 0,859 1,295*
Breakeven trading cost Unprofitable 0,46 % 0,20 %
Number of signals generated 2 79 334
OBV Kelly criterion
Average daily return of a strategy 0,06 % 0,20 %
Standard deviation of daily return 1,44 % 5,06 %
Z statistic 1,263 1,121
Breakeven trading cost 0,16 % 0,75 %
Number of signals generated 387 213
71
Appendix 36: Statistics for Colombia ETF. ***, **, * represent statistical significance at 1%, 5%,10% level, respectively.
Technical indicator statistics are from both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample
(4th of January 2016 to 19th of June 2020) periods, Kelly criterion is only for an out-of-sample period.
Colombia RSI TRB50 TRB150
Average daily return of a strategy -0,08 % -0,03 % -0,08%
Standard deviation of daily return 1,87 % 1,12 % 1,09%
Z statistic -1,381 -0,940 -2,200
Breakeven trading cost Unprofitable Unprofitable Unprofitable
Number of signals generated 13 16 6
TRB200 MACD STOCH-D
Average daily return of a strategy -0,02% 0,05 % 0,08 %
Standard deviation of daily return 1,09% 1,41 % 1,57 %
Z statistic -0,461 1,191 1,678**
Breakeven trading cost Unprofitable 0,71 % 0,25 %
Number of signals generated 2 77 347
OBV Kelly criterion
Average daily return of a strategy 0,07 % 0,23 %
Standard deviation of daily return 1,52 % 6,27 %
Z statistic 1,459* 1,134
Breakeven trading cost 0,21 % 0,57 %
Number of signals generated 342 385
72
Appendix 37: Trading rule results for Taiwan ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Taiwan In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 25,6% 66,6% -14,0% 0,6% -18,5% 15,4% 3,8% 2,7% 1,4% 65,8% -12,9% 26,6% -17,8% -5,3% 190,4% -6,0% 59,9%
Annualized performance 5,4% 12,1% -3,4% 2,9% -4,6% 3,3% 0,9% 0,6% 0,3% 12,0% -3,1% 5,4% -4,4% -1,2% 27,0% -1,4% 11,1%
Highest open drawdown
(HOD) 5,2% 0,0% 14,0% 18,8% 18,5% 6,0% 9,3% 1,4% 4,6% 0,8% 23,9% 7,7% 29,1% 9,6% 0,0% 17,4% 8,4%
Standard deviation of daily
returns 1,0% 1,1% 0,7% 0,8% 0,7% 0,8% 0,7% 0,9% 0,8% 0,8% 0,8% 1,0% 0,8% 0,9% 2,4% 1,2% 1,3%
Buy and hold index 33,7% 4,2% -8,5% -37,1% -13,3% -27,8% 10,5% -35,8% 7,9% 3,7% -7,3% -20,8% -12,5% -40,8% 81,6% 0,0% 0,0%
Profit/loss index 48,7 66,6 -58,0 7,4 -92,4 58,5 3,8 13,8 1,8 45,9 -5,4 6,6 -7,0 -1,3 39,0 - -
Reward/risk index 83,2% 100,0% -100,0% 3,3% -100,0% 72,1% 29,1% 66,8% 22,8% 98,9% -54,2% 77,6% -61,1% -55,3% 100,0% -34,6% 87,7%
Sharpe ratio 0,33 0,64 -0,32 0,14 -0,44 0,17 0,07 -0,04 0,03 0,84 -0,24 0,28 -0,33 -0,16 0,69 -0,07 0,48
Total trades 5 6 7 9 3 2 1 2 44 42 179 177 229 239 50 - -
Avg. Profit/Avg. Loss 0,63 - 1,22 1,48 0,18 3,04 - 1,39 1,54 2,68 1,58 1,50 1,87 1,57 3,31 - -
Profitable trades 80,0% 100,0% 28,6% 44,4% 33,3% 50,0% 100,0% 50,0% 40,9% 47,6% 36,3% 44,1% 31,9% 38,9% 54,0% - -
73
Appendix 38: Trading rule results for China ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
China In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 30,2% 47,8% -5,3% 7,4% -19,8% 14,9% -21,6% 2,0% 23,9% 0,0% -22,5% 13,7% -3,5% 72,0% 138,9% 5,2% 48,2%
Annualized performance 6,3% 9,2% -1,2% 37,8% -5,0% 3,2% -5,5% 0,4% 5,1% 0,0% -5,7% 2,9% -0,8% 12,9% 21,6% 1,2% 9,2%
Highest open drawdown
(HOD) 11,9% 12,2% 8,3% 7,6% 19,8% 4,3% 25,6% 10,4% 2,8% 13,2% 26,6% 11,8% 19,3% 9,8% 42,1% 23,0% 17,2%
Standard deviation of daily
returns 1,2% 1,1% 0,9% 0,9% 0,9% 1,0% 1,0% 0,9% 1,0% 0,9% 1,1% 1,0% 1,1% 1,0% 3,0% 1,5% 1,4%
Buy and hold index 23,8% -0,2% -9,9% -27,5% -23,7% -22,5% -25,4% -31,1% 17,8% -32,5% -26,3% -23,3% -8,3% 16,1% 61,3% 0,0% 0,0%
Profit/loss index 63,8 84,7 -29,3 73,2 -100,0 38,3 -100,0 7,0 14,1 0,0 -6,2 3,0 -0,8 13,1 10,6 - -
Reward/risk index 71,8% 79,7% -63,8% 49,6% -100,0% 77,7% -84,2% 16,2% 89,4% -0,4% -84,6% 53,7% -18,2% 88,0% 76,7% 18,4% 73,6%
Sharpe ratio 0,32 0,46 -0,09 2,56 -0,34 0,12 -0,35 -0,05 0,32 -0,08 -0,34 0,11 -0,05 0,75 0,42 0,05 0,35
Total trades 5 5 8 6 3 2 2 2 45 46 176 193 202 189 57 - -
Avg. Profit/Avg. Loss 0,88 2,16 1,29 5,09 - 2,25 - 1,39 2,05 1,65 1,60 1,61 2,03 2,07 2,48 - -
Profitable trades 80,0% 80,0% 37,5% 50,0% 0,0% 50,0% 0,0% 50,0% 40,0% 39,1% 35,8% 40,9% 33,2% 41,3% 43,9% - -
74
Appendix 39: Trading rule results for New Zealand ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
New Zealand In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -1,0% 21,8% 23,9% 8,5% -4,8% 10,9% 16,6% 16,8% -1,9% 37,0% -35,0% 26,7% -15,9% 1,1% 248,4% 16,5% 46,0%
Annualized performance -0,2% 4,5% 5,1% 43,9% -1,1% 2,3% 3,6% 3,5% -0,4% 7,3% -9,5% 5,4% -3,9% 0,2% 32,3% 3,6% 8,9%
Highest open drawdown
(HOD) 11,6% 19,2% 0,3% 0,0% 12,2% 0,0% 0,0% 0,4% 13,6% 1,5% 41,3% 3,2% 20,7% 5,8% 4,1% 17,1% 7,7%
Standard deviation of daily
returns 0,7% 1,1% 0,7% 0,8% 0,7% 0,8% 0,8% 0,8% 0,8% 0,8% 0,8% 1,1% 0,8% 0,8% 2,6% 1,1% 1,4%
Buy and hold index -15,0% -16,6% 6,4% -25,7% -18,2% -24,1% 0,1% -20,0% -15,8% -6,2% -44,2% -13,2% -27,8% -30,8% 138,6% 0,0% 0,0%
Profit/loss index -6,9 21,8 65,5 98,4 -50,0 10,9 16,6 94,4 -2,1 31,9 -18,0 8,5 -8,8 0,5 19,5 - -
Reward/risk index -9,0% 53,2% 98,7% 100,0% -39,1% 100,0% 100,0% 97,6% -13,8% 96,1% -84,7% 89,3% -76,9% 15,9% 98,4% 49,0% 85,7%
Sharpe ratio -0,02 0,19 0,45 3,45 -0,10 0,09 0,29 0,18 -0,04 0,49 -0,74 0,25 -0,32 -0,07 0,74 0,20 0,35
Total trades 3 3 7 6 3 3 1 2 46 47 193 182 194 192 250 - -
Avg. Profit/Avg. Loss 0,52 - 1,39 15,88 1,09 - - 21,15 2,12 2,61 1,66 2,02 1,56 2,01 4,16 - -
Profitable trades 66,7% 100,0% 71,4% 83,3% 33,3% 100,0% 100,0% 50,0% 32,6% 40,4% 30,6% 38,5% 36,1% 33,9% 37,2% - -
75
Appendix 40: Trading rule results for Netherlands ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Netherlands In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 23,3% 12,1% -13,1% 5,2% 17,6% 8,6% 7,5% 5,9% 11,8% 37,6% -8,5% 34,6% -5,8% 30,7% 130,4% 31,4% 38,0%
Annualized performance 5,0% 2,6% -3,2% 25,4% 3,8% 1,9% 1,7% 1,3% 2,6% 7,4% -2,0% 6,9% -1,4% 6,2% 20,6% 6,5% 7,5%
Highest open drawdown
(HOD) 4,8% 20,2% 17,0% 6,6% 5,7% 14,4% 5,7% 14,3% 13,3% 2,7% 11,8% 3,6% 11,2% 2,7% 90,4% 14,2% 8,1%
Standard deviation of daily
returns 0,8% 1,0% 1,0% 0,7% 0,7% 0,8% 0,7% 0,7% 0,9% 0,7% 0,9% 0,9% 1,0% 0,8% 7,5% 1,3% 1,2%
Buy and hold index -6,2% -18,7% -33,9% -23,7% -10,5% -21,3% -18,2% -23,3% -14,9% -0,3% -30,4% -2,4% -28,3% -5,3% 67,0% 0,0% 0,0%
Profit/loss index 23,3 66,6 -50,2 60,1 356,0 45,4 57,1 30,8 13,0 31,5 -3,1 11,5 -1,9 10,3 10,5 - -
Reward/risk index 82,8% 37,5% -77,4% 44,2% 75,5% 37,5% 56,7% 29,0% 47,0% 93,3% -72,3% 90,6% -51,7% 91,8% 59,0% 68,9% 82,5%
Sharpe ratio 0,39 0,09 -0,21 2,34 0,33 0,05 0,14 0,01 0,19 0,55 -0,14 0,39 -0,09 0,38 0,16 0,32 0,32
Total trades 3 4 9 7 2 2 2 2 43 46 181 177 187 200 52 - -
Avg. Profit/Avg. Loss - 3,36 0,71 2,34 4,80 2,18 1,81 1,72 2,14 2,50 1,44 1,54 1,72 1,97 2,79 - -
Profitable trades 100,0% 50,0% 44,4% 57,1% 50,0% 50,0% 50,0% 50,0% 37,2% 41,3% 39,8% 45,2% 36,4% 39,0% 42,3% - -
76
Appendix 41: Trading rule results for Switzerland ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Switzerland In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 10,2% 16,9% 27,0% 3,2% 17,8% 10,3% 8,7% 7,7% 13,8% 9,3% 42,5% -1,5% 18,1% -18,1% 65,8% 25,2% 27,6%
Annualized performance 2,3% 3,6% 5,7% 15,1% 3,8% 2,2% 1,9% 1,7% 3,0% 2,0% 8,5% -0,3% 3,9% -4,4% 12,0% 5,3% 5,6%
Highest open drawdown (HOD) 5,5% 9,0% 1,6% 5,7% 12,8% 0,4% 3,5% 1,0% 5,8% 5,9% 3,3% 14,9% 5,1% 21,0% 81,4% 16,4% 9,6%
Standard deviation of daily
returns 0,6% 0,9% 0,6% 0,6% 0,7% 0,5% 0,6% 0,6% 0,7% 0,6% 0,7% 0,7% 0,7% 0,7% 5,1% 1,0% 1,1%
Buy and hold index -12,0% -8,4% 1,4% -19,1% -5,9% -13,6% -13,2% -15,6% -9,2% -14,3% 13,8% -22,8% -5,6% -35,8% 29,9% 0,0% 0,0%
Profit/loss index 62,8 99,0 75,3 52,2 210,0 10,3 73,8 7,7 17,3 13,5 16,9 -1,0 8,2 -9,9 0,1 - -
Reward/risk index 65,1% 65,3% 94,3% 36,0% 58,1% 95,8% 71,3% 88,4% 70,4% 61,4% 92,8% -9,9% 78,1% -86,0% 44,7% 60,6% 74,2%
Sharpe ratio 0,24 0,17 0,55 1,58 0,33 0,12 0,19 0,05 0,27 0,09 0,76 -0,13 0,33 -0,48 0,14 0,32 0,26
Total trades 4 5 5 8 2 2 2 2 44 51 178 178 206 233 191 - -
Avg. Profit/Avg. Loss 1,00 76,35 3,40 2,40 3,39 - 1,97 - 1,59 1,78 1,84 1,17 1,79 1,33 1,30 - -
Profitable trades 75,0% 60,0% 60,0% 50,0% 50,0% 100,0% 50,0% 100,0% 45,5% 41,2% 43,3% 46,1% 39,8% 39,1% 46,6% - -
77
Appendix 42: Trading rule results for Japan ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Japan In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 23,2% 21,2% -10,0% 0,6% -16,4% 9,5% 7,7% 6,1% 9,9% 14,5% -15,0% -0,1% 5,9% 3,6% 116,3% 24,2% 17,4%
Annualized performance 4,9% 4,4% -2,4% 2,6% -4,0% 2,1% 1,7% 1,3% 2,2% 3,1% -3,7% 0,0% 1,3% 0,8% 18,9% 5,1% 3,7%
Highest open drawdown
(HOD) 7,2% 8,0% 14,2% 6,4% 16,4% 1,4% 0,6% 4,0% 6,3% 11,3% 23,6% 11,7% 6,4% 17,5% 52,3% 11,4% 13,5%
Standard deviation of daily
returns 0,8% 1,0% 0,7% 0,6% 0,8% 0,5% 0,8% 0,5% 0,8% 0,7% 0,8% 0,7% 0,8% 0,7% 5,0% 1,1% 1,1%
Buy and hold index -0,8% 3,2% -27,5% -14,3% -32,7% -6,7% -13,3% -9,7% -11,5% -2,5% -31,5% -14,9% -14,7% -11,7% 84,2% 0,0% 0,0%
Profit/loss index 23,2 21,2 -36,2 11,7 -61,6 58,6 935,4 48,2 11,6 17,6 -7,6 -0,1 2,2 1,9 0,9 - -
Reward/risk index 76,3% 72,7% -70,0% 8,1% -100,0% 87,3% 92,7% 60,4% 61,2% 56,2% -63,4% -1,2% 47,9% 17,1% 69,0% 68,0% 56,3%
Sharpe ratio 0,38 0,21 -0,22 0,14 -0,32 0,10 0,13 0,02 0,18 0,17 -0,30 -0,11 0,11 -0,04 0,23 0,29 0,14
Total trades 4 4 9 9 4 2 2 2 45 45 185 187 230 224 284 - -
Avg. Profit/Avg. Loss - - 0,92 1,52 1,42 2,80 10,44 2,17 3,04 2,46 1,64 1,44 1,95 1,90 2,19 - -
Profitable trades 100,0% 100,0% 44,4% 44,4% 25,0% 50,0% 50,0% 50,0% 28,9% 35,6% 34,1% 40,6% 34,8% 35,3% 36,6% - -
78
Appendix 43: Trading rule results for Sweden ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Sweden In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 11,3% 41,5% -14,3% 0,0% -2,5% -6,9% -7,7% -11,8% -19,7% 4,4% -4,4% -10,3% 7,2% -13,8% -6,2% 10,7% 6,7%
Annualized performance 2,5% 8,1% -3,5% 0,0% -0,6% -1,6% -1,8 % -2,8% -4,9% 1,0% -1,0% -2,4% 1,6% -3,3% -1,4% 2,4% 1,5%
Highest open drawdown
(HOD) 5,3% 5,9% 23,3% 10,3% 2,5% 6,9% 7,7% 11,8% 19,7% 19,8% 15,2% 23,0% 5,1% 25,9% 83,4% 16,2% 24,8%
Standard deviation of daily
returns 0,9% 1,3% 1,1% 0,7% 0,8% 0,6% 0,8% 0,6% 1,0% 0,9% 1,1% 1,0% 1,1% 1,0% 5,6% 1,5% 1,5%
Buy and hold index 0,5% 32,6% -22,6% -6,3% -12,0% -12,7% -16,7% -17,4% -27,5% -2,1% -13,6% -15,9% -3,2% -19,2% -12,0% 0,0% 0,0%
Profit/loss index 32,8 41,5 -71,0 0,0 -18,8 -41,6 -48,1 -65,8 -17,6 4,8 -1,3 -3,0 2,0 -4,5 -2,1 - -
Reward/risk index 68,3% 87,5% -61,2% 0,0% -100,0% -100,0% -100,0% -100,0% -100,0% 18,3% -28,6% -44,7% 58,3% -53,3% -7,4% 39,8% 21,2%
Sharpe ratio 0,18 0,34 -0,20 -0,10 -0,05 -0,28 -0,15 -0,40 -0,30 -0,02 -0,06 -0,22 0,09 -0,29 -0,03 0,10 0,01
Total trades 3 6 7 8 2 2 2 2 49 40 175 181 196 224 232 - -
Avg. Profit/Avg. Loss 0,93 - 2,02 1,85 0,94 0,70 0,62 0,42 2,07 0,92 1,50 1,35 1,78 1,64 1,63 - -
Profitable trades 66,7% 100,0% 14,3% 37,5% 50,0% 50,0% 50,0% 50,0% 28,6% 55,0% 40,0% 40,9% 37,8% 36,2% 37,9% - -
79
Appendix 44: Trading rule results for Israel ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Israel In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 18,4% 35,0% -16,2% -0,6% -2,9% -21,1% -1,2% -15,5% -2,4% 44,3% 3,0% -12,5% -16,3% -25,2% 90,0% 8,2% 10,4%
Annualized performance 4,0% 7,0% -4,0% -2,8% -0,7% -5,2% -0,3% -3,7% -0,6% 8,6% 0,7% -2,9% -4,0% -6,3% 15,5% 1,8% 2,2%
Highest open drawdown
(HOD) 12,4% 6,6% 25,8% 6,8% 6,5% 21,1% 7,1% 15,5% 5,9% 5,3% 13,1% 21,2% 22,2% 30,0% 51,1% 22,6% 21,8%
Standard deviation of daily
returns 0,5% 1,1% 0,8% 0,6% 0,7% 0,6% 0,7% 0,7% 0,8% 0,7% 0,8% 0,9% 0,8% 0,8% 4,7% 1,1% 1,3%
Buy and hold index 9,4% 22,2% -22,6% -10,0% -10,2% -28,5% -8,6% -23,5% -9,8% 30,6% -4,7% -20,7% -22,6% -32,2% 72,0% 0,0% 0,0%
Profit/loss index 83,3 92,3 -50,5 -23,6 -30,3 -100,0 -16,5 -100,0 -3,3 45,9 1,5 -5,7 -10,7 -15,5 2,0 - -
Reward/risk index 59,8% 84,0% -62,8% -9,4% -44,5% -100,0% -16,7% -100,0% -40,6% 89,4% 18,8% -59,0% -73,3% -83,9% 63,8% 26,6% 32,4%
Sharpe ratio 0,48 0,33 -0,32 -0,40 -0,06 -0,70 -0,03 -0,46 -0,04 0,68 0,06 -0,30 -0,33 -0,56 0,19 0,10 0,05
Total trades 4 7 7 9 3 3 2 2 48 42 173 185 179 179 237 - -
Avg. Profit/Avg. Loss 6,89 2,60 0,46 1,01 1,50 - 0,90 - 2,43 2,76 1,88 1,24 1,49 1,54 1,54 - -
Profitable trades 50,0% 85,7% 57,1% 44,4% 33,3% 0,0% 50,0% 0,0% 29,2% 45,2% 35,8% 42,7% 36,3% 34,1% 43,9% - -
80
Appendix 45: Trading rule results for Germany ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Germany In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 9,3% 1,0% 7,6% 4,6% 9,6% -5,4% 19,1% -6,6% -7,5% 0,7% 16,4% -2,5% -19,9% 30,3% -4,3% 28,4% 4,7%
Annualized performance 2,1% 0,2% 1,7% 22,4% 2,1% -1,2% 4,1% -1,5% -1,8% 0,2% 3,6% -0,6% -5,0% 6,1% -1,0% 5,9% 1,0%
Highest open drawdown
(HOD) 6,4% 31,2% 20,9% 9,6% 20,5% 5,4% 0,2% 6,6% 10,4% 18,3% 6,2% 10,4% 24,5% 7,8% 94,2% 14,5% 29,5%
Standard deviation of daily
returns 0,7% 1,2% 1,0% 0,7% 1,0% 0,6% 0,6% 0,6% 1,0% 0,9% 1,0% 1,0% 1,1% 1,0% 14,3% 1,5% 1,4%
Buy and hold index -14,8% -3,4% -16,2% 0,0% -14,6% -9,6% -7,3% -10,7% -27,9% -3,8% -9,4% -6,8% -37,6% 24,5% -8,6% 0,0% 0,0%
Profit/loss index 74,1 5,8 24,0 63,8 82,9 -29,6 19,1 -36,1 -7,8 0,7 4,4 -0,8 -5,3 7,7 -0,3 - -
Reward/risk index 59,4% 3,3% 26,7% 32,7% 32,0% -100,0% 98,9% -100,0% -71,8% 3,6% 72,6% -23,8% -81,5% 79,6% -4,6% 66,2% 13,6%
Sharpe ratio 0,19 -0,05 0,11 2,04 0,14 -0,25 0,42 -0,27 -0,11 -0,08 0,23 -0,11 -0,29 0,31 -0,01 0,25 -0,01
Total trades 3 4 7 6 2 2 1 2 41 49 169 176 224 206 237 - -
Avg. Profit/Avg. Loss 8,65 1,17 1,99 3,37 2,07 0,86 - 0,78 2,04 2,00 1,70 1,45 1,90 1,79 1,44 - -
Profitable trades 33,3% 50,0% 42,9% 50,0% 50,0% 50,0% 100,0% 50,0% 31,7% 34,7% 40,2% 40,9% 32,1% 41,3% 40,1% - -
81
Appendix 46: Trading rule results for South Korea ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
South Korea In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 40,2 % 15,2 % 6,1% 2,6% -17,9% -6,1% -21,9% 18,6% -7,5% 11,2% 30,8% -37,7% -12,2% -22,0% -109,0% -11,3% 17,0%
Annualized performance 8,1 % 3,2 % 1,4% 11,9% -4,4% -1,4% -5,6% 3,9% -1,8% 2,4% 6,4% -10,1% -3,0% -5,4% - -2,7% 3,6%
Highest open drawdown
(HOD) 0,9 % 23,5 % 14,7% 4,3% 19,4% 6,1% 22,1% 7,8% 12,0% 12,9% 5,1% 38,8% 23,0% 28,9% 204,5% 20,2% 19,7%
Standard deviation of daily
returns 1,2 % 1,3 % 0,9% 0,9% 0,8% 1,0% 0,6% 0,8% 0,9% 1,0% 0,9% 1,2% 0,9% 1,0% 17,2% 1,4% 1,6%
Buy and hold index 58,1 % -1,6 % 19,7% -12,3% -7,4% -19,8% -12,0% 1,4% 4,4% -5,0% 47,5% -46,7% -1,0% -33,3% -107,7% 0,0% 0,0%
Profit/loss index 76,2 75,0 41,4 34,9 -100,0 -24,8 -100,0 18,6 -8,9 9,2 8,0 -10,8 -3,6 -6,2 -0,1 - -
Reward/risk index 97,9 % 39,2 % 29,3 % 37,3% -92,1% -100,0% -99,1% 70,5% -62,1% 46,4% 85,8% -97,1% -52,9% -76,2% -53,3% -56,1% 46,3%
Sharpe ratio 0,44 0,10 0,10 0,75 -0,36 -0,17 -0,60 0,21 -0,13 0,07 0,43 -0,60 -0,20 -0,40 - -0,13 0,09
Total trades 5 4 6 7 2 2 2 1 46 44 174 188 232 243 199 - -
Avg. Profit/Avg. Loss 3,79 4,62 1,85 1,33 - 1,00 - - 1,74 1,93 1,99 1,20 1,86 1,69 1,26 - -
Profitable trades 60,0 % 50,0 % 50,0 % 57,1% 0,0% 50,0% 0,0% 100,0% 34,8% 38,6% 38,5% 39,4% 33,6% 34,2% 40,2% - -
82
Appendix 47: Trading rule results for Belgium ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Belgium In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 35,4% -4,9% 16,3% 3,9% 23,8% 1,0% 24,9% -4,1% 5,0% 3,9% 15,1% 18,4% -2,6% -6,1% 4,3% 46,2% -7,0%
Annualized performance 7,2% -1,1% 3,6% 18,5% 5,1% 0,2% 5,3% -0,9% 1,1% 0,9% 3,3% 3,9% -0,6% -1,4% 0,9% 9,2% -1,6%
Highest open drawdown
(HOD) 2,8% 31,9% 5,5% 3,6% 2,7% 10,3% 2,7% 10,3% 11,6% 11,5% 11,8% 5,8% 23,1% 23,0% 99,6% 17,2% 30,5%
Standard deviation of daily
returns 0,7% 1,0% 0,8% 0,6% 0,7% 0,6% 0,8% 0,7% 0,8% 0,7% 0,8% 0,9% 0,9% 0,9% 22,9% 1,2% 1,3%
Buy and hold index -7,4% 2,3% -20,5% 11,7% -15,3% 8,5% -14,6% 3,1% -28,2% 11,7% -21,3% 27,3% -33,4% 0,9% 12,1% 0,0% 0,0%
Profit/loss index 35,4 -26,9 49,5 77,8 496,0 7,9 24,9 -41,0 7,5 5,3 5,6 6,6 -1,0 -2,9 -1,4 - -
Reward/risk index 92,6% -15,3% 74,7% 51,9% 89,8% 8,5% 90,2% -39,7% 30,0% 25,6% 56,0% 76,0% -11,2% -26,7% 4,1% 72,9% -22,9%
Sharpe ratio 0,63 -0,14 0,27 1,72 0,43 -0,10 0,43 -0,21 0,09 -0,03 0,25 0,19 -0,04 -0,19 0,00 0,48 -0,14
Total trades 3 4 8 7 2 2 2 2 43 44 172 184 183 167 192 - -
Avg. Profit/Avg. Loss - 0,82 1,40 3,92 6,26 1,22 - 0,66 1,76 1,61 1,57 1,72 1,50 1,37 1,51 - -
Profitable trades 100,0% 50,0% 62,5% 57,1% 50,0% 50,0% 100,0% 50,0% 39,5% 40,9% 41,3% 40,2% 39,9% 41,9% 40,6% - -
83
Appendix 48: Trading rule results for Turkey ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Turkey In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 38,6% 4,1% -33,8% -3,4% -9,1% -37,4% 2,2% -30,5% -13,3% -34,9% 3,8% -31,1% 4,4% -25,4% -58,2% -25,3% -35,6%
Annualized performance 7,8% 0,9% -9,1% -14,3% -2,2% -10,0% 0,5% -7,8% -3,2% -9,2% 0,9% -8,0% 1,0% -6,4% -17,8% -6,5% -9,4%
Highest open drawdown
(HOD) 1,9% 35,0% 33,8% 18,8% 14,7% 37,4% 2,7% 30,5% 20,3% 48,6% 26,2% 45,9% 37,5% 37,7% 93,1% 29,1% 49,6%
Standard deviation of daily
returns 1,4% 1,9% 1,1% 1,2% 1,1% 1,0% 0,7% 0,9% 1,3% 1,4% 1,4% 1,5% 1,2% 1,4% 8,1% 2,0% 2,1%
Buy and hold index 85,6% 61,7% -11,4% 50,1% 21,7% -2,8% 36,8% 8,0% 16,1% 1,2% 39,0% 7,1% 39,8% 15,9% -35,1% 0,0% 0,0%
Profit/loss index 55,3 6,6 -64,2 -40,3 -44,6 -100,0 2,2 -100,0 -8,2 -23,1 0,5 -5,2 0,6 -3,3 -7,1 - -
Reward/risk index 95,2% 10,4% -100,0% -18,1% -62,4% -100,0% 44,6% -100,0% -65,5% -71,7% 12,7% -67,8% 10,6% -67,4% -62,5% -87,1% -71,9%
Sharpe ratio 0,36 -0,01 -0,50 -0,84 -0,13 -0,68 0,04 -0,61 -0,16 -0,46 0,04 -0,40 0,05 -0,33 -0,15 -0,21 -0,31
Total trades 6 7 8 8 2 3 1 2 45 50 168 185 209 224 236 - -
Avg. Profit/Avg. Loss 3,04 0,97 0,88 1,24 0,70 - - - 1,72 1,73 1,81 1,25 1,72 1,80 1,58 - -
Profitable trades 50,0% 57,1% 37,5% 37,5% 50,0% 0,0% 100,0% 0,0% 35,6% 30,0% 36,9% 41,1% 38,3% 33,9% 33,9% - -
84
Appendix 49: Trading rule results for Ireland ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Ireland In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 16,4% -6,3% 98,6% 3,3% 73,8% -3,8% 57,0% -6,5% 37,2% -11,9% 10,8% 15,5% 7,0% -5,2% 11,4% 112,3% -7,2%
Annualized performance 3,6% -1,4% 17,2% 15,4% 13,6% -0,9% 11,0% -1,5% 7,6% -2,8% 2,4% 3,3% 1,6% -1,2% 2,5% 19,0% -1,7%
Highest open drawdown
(HOD) 5,5% 36,5% 12,6% 11,9% 15,3% 3,8% 18,9% 6,5% 9,5% 28,8% 9,0% 10,0% 4,2% 29,2% 112,5% 13,6% 37,4%
Standard deviation of daily
returns 0,6% 1,3% 1,1% 0,6% 1,0% 0,6% 1,0% 0,6% 0,9% 0,9% 0,9% 1,0% 0,9% 1,0% 50,3% 1,3% 1,4%
Buy and hold index -45,1% 0,9% -6,4% 11,2% -18,1% 3,6% -26,0% 0,8% -35,4% -5,1% -47,8% 24,4% -49,6% 2,1% 20,0% 0,0% 0,0%
Profit/loss index 90,9 -36,4 99,4 45,6 73,8 -26,0 57,0 -43,9 25,5 -15,1 3,9 5,1 2,3 -2,0 -7,8 - -
Reward/risk index 74,8% -17,2% 88,7% 21,5% 82,8% -100,0% 75,1% -100,0% 79,6% -41,4% 54,5% 60,8% 62,6% -17,8% 9,2% 89,2% -19,2%
Sharpe ratio 0,38 -0,13 1,01 1,38 0,84 -0,22 0,69 -0,29 0,54 -0,28 0,17 0,13 0,11 -0,16 0,00 0,91 -0,13
Total trades 3 4 5 7 2 2 2 2 50 53 178 174 189 185 66 - -
Avg. Profit/Avg. Loss 6,22 0,71 67,18 1,62 - 0,87 - 0,66 2,66 2,13 1,83 1,59 1,88 1,44 2,20 - -
Profitable trades 66,7% 50,0% 80,0% 57,1% 100,0% 50,0% 100,0% 50,0% 38,0% 28,3% 37,6% 42,0% 36,5% 41,1% 33,3% - -
85
Appendix 50: Trading rule results for France ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
France In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -1,1% -2,4% -14,2% 1,9% 2,9% 9,3% 15,9% 8,7% -11,6% -2,6% -10,1% 30,8% -23,4% 22,2% 75,5% 8,8% 13,3%
Annualized performance -0,2% -0,6% -3,5% 8,7% 0,7% 2,0% 3,5% 1,9% -2,8% -0,6% -2,4% 6,2% -6,0% 4,6% 13,4% 2,0% 2,8%
Highest open drawdown (HOD) 4,4% 27,7% 22,1% 11,3% 0,7% 1,7% 0,7% 2,2% 24,7% 18,1% 13,2% 5,7% 30,2% 17,3% 10,9% 19,1% 15,9%
Standard deviation of daily
returns 0,9% 1,2% 1,0% 0,7% 0,8% 0,6% 0,7% 0,6% 1,0% 0,8% 1,1% 1,0% 1,0% 0,9% 2,7% 1,5% 1,4%
Buy and hold index -9,1% -13,9% -21,2% -10,1% -5,5% -3,6% 6,5% -4,0% -18,8% -14,1% -17,4% 15,5% -29,6% 7,8% 54,8% 0,0% 0,0%
Profit/loss index -6,4 -12,5 -59,7 22,6 25,6 50,1 15,9 48,8 -11,9 -3,0 -3,1 9,0 -7,5 6,1 3,0 - -
Reward/risk index -23,7% -8,8% -64,4% 14,4% 80,4% 84,4% 95,8% 80,0% -46,9% -14,6% -76,6% 84,3% -77,6% 56,2% 87,4% 31,5% 45,5%
Sharpe ratio -0,02 -0,09 -0,22 0,68 0,05 0,09 0,33 0,08 -0,18 -0,13 -0,15 0,30 -0,37 0,23 0,29 0,08 0,07
Total trades 2 4 8 8 2 2 1 2 44 49 175 175 218 212 181 - -
Avg. Profit/Avg. Loss 1,09 0,97 0,28 1,51 1,42 2,37 - 2,30 1,24 1,34 1,58 1,38 1,75 1,99 1,71 - -
Profitable trades 50,0% 50,0% 62,5% 50,0% 50,0% 50,0% 100,0% 50,0% 40,9% 42,9% 37,7% 46,3% 32,6% 37,3% 46,4% - -
86
Appendix 51: Trading rule results for Canada ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Canada In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -0,2% 19,2% -4,7% 1,0% -11,7% 4,7% -16,7% -0,6% -4,3% 49,7% 16,6% 34,3% -8,0% 37,2% 133,0% -28,0% 21,2%
Annualized performance -0,1% 4,0% -1,1% 4,7% -2,8% 1,0% -4,1% -0,1% -1,0% 9,5% 3,6% 6,8% -1,9% 7,3% 20,9% -7,3% 4,4%
Highest open drawdown
(HOD) 5,0% 21,9% 6,6% 2,1% 14,7% 6,0% 16,7% 7,9% 7,2% 0,0% 5,5% 5,3% 16,5% 5,5% 21,7% 28,5% 16,7%
Standard deviation of daily
returns 1,0% 1,1% 0,5% 0,7% 0,4% 0,6% 0,5% 0,6% 0,7% 0,8% 0,8% 0,9 % 0,8% 0,8% 2,3% 1,1% 1,3%
Buy and hold index 38,5% -1,7% 32,3% -16,7% 22,6% -13,6% 15,7% -18,0% 32,8% 23,5% 61,8% 10,8% 27,8% 13,2% 92,2% 0,0% 0,0%
Profit/loss index -0,7 73,3 -34,5 31,4 -100,0 51,3 -100,0 -11,3 -6,7 42,6 7,1 11,4 -3,6 13,1 0,8 - -
Reward/risk index -4,4% 46,6% -71,2% 33,1% -79,4% 44,1% -100,0% -7,5% -60,1% 100,0% 75,2% 86,6% -48,3% 87,1% 86,0% -98,0% 56,0%
Sharpe ratio 0,00 0,16 -0,15 0,33 -0,43 -0,02 -0,49 -0,13 -0,09 0,69 0,28 0,42 -0,16 0,47 0,53 -0,42 0,15
Total trades 5 5 6 8 2 2 2 2 38 42 167 173 209 215 186 - -
Avg. Profit/Avg. Loss 0,76 2,91 1,42 0,93 - 2,24 - 0,94 0,86 2,94 1,83 1,43 1,69 1,98 1,42 - -
Profitable trades 60,0% 60,0% 33,3% 62,5% 0,0% 50,0% 0,0% 50,0% 52,6% 42,9% 38,9% 47,4% 35,9% 40,0% 48,4% - -
87
Appendix 52: Trading rule results for Italy ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Italy In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 1,5% -26,0% -33,5% -0,3% -3,0% -6,6% 7,0% -9,2% 4,8% 28,3% -18,3% -25,5% -43,0% -20,7% -14,3% 2,6% -11,8%
Annualized performance 0,3% -6,5% -9,0% -1,4% -0,7% -1,5% 1,6% -2,1% 1,1% 5,7% -4,6% -6,4% -12,2% -5,1% -3,4% 0,6% -2,8%
Highest open drawdown
(HOD) 22,5% 43,3% 37,3% 20,3% 15,0% 6,8% 16,0% 9,2% 24,6% 17,9% 19,3% 37,0% 43,2% 39,0% 71,7% 30,7% 33,2%
Standard deviation of daily
returns 1,2% 1,5% 1,2% 0,8% 1,1% 0,8% 0,9% 0,7% 1,3% 1,0% 1,3% 1,2% 1,3 % 1,1% 5,0% 1,9% 1,7%
Buy and hold index -1,1% -16,1% -35,2% 13,0% -5,5% 5,8% 4,3% 2,9% 2,2% 45,4% -20,4% -15,6% -44,4% -10,1% -2,9% 0,0% 0,0%
Profit/loss index 6,6 -77,6 -76,1 -4,0 -24,5 -34,3 7,0 -46,9 2,9 22,0 -3,6 -6,8 -9,5 -6,1 -2,2 - -
Reward/risk index 6,2% -60,1% -89,8% -1,5% -20,2% -97,2% 30,5% -100,0% 16,4% 61,2% -95,1% -68,9% -99,4% -53,0% -20,0% 7,8% -35,5%
Sharpe ratio 0,02 -0,33 -0,46 -0,20 -0,04 -0,21 0,11 -0,28 0,05 0,28 -0,22 -0,42 -0,57 -0,36 -0,06 0,02 -0,15
Total trades 3 4 8 9 2 2 1 2 40 37 185 186 219 214 229 - -
Avg. Profit/Avg. Loss 0,63 0,89 0,56 1,35 0,86 0,82 - 0,66 1,38 1,46 1,61 1,49 1,50 1,71 1,50 - -
Profitable trades 66,7% 25,0% 37,5% 44,4% 50,0% 50,0% 100,0% 50,0% 45,0% 51,4% 36,8% 37,1% 34,2% 34,6% 39,3% - -
88
Appendix 53: Trading rule results for Malaysia ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Malaysia In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -29,0% -19,7% -13,1% -3,2% -14,2% -0,2% -19,8% -2,0% -19,7% 2,0% -35,4% -27,6% -21,6% -22,0% -69,5% -45,1% -15,3%
Annualized performance -7,6% -4,8% -3,2% -13,5% -3,5% 0,0% -5,0% -0,4% -4,9% 0,4% -9,6% -7,0% -5,5% -5,4% -23,4% -12,9% -3,7%
Highest open drawdown
(HOD) 30,7% 40,6% 13,1% 16,1% 14,2% 3,2% 19,8% 3,9% 24,5% 13,5% 35,8% 29,8% 21,6% 27,0% 107,3% 46,5% 36,0%
Standard deviation of
daily returns 1,3% 1,1% 0,6% 0,6% 0,6% 0,4% 0,5% 0,4% 0,8% 0,8% 1,1% 0,9 % 1,1% 0,9% 13,5% 1,4% 1,3%
Buy and hold index 29,4% -5,2% 58,3% 14,3% 56,3% 17,8% 46,2% 15,7% 46,2% 20,4% 17,7% -14,5% 42,8% -7,9% -64,0% 0,0% 0,0%
Profit/loss index -67,7 -100,0 -89,9 -52,9 -100,0 -100,0 -100,0 -100,0 -35,2 2,7 -19,0 -15,1 -9,8 -9,9 -6,9 - -
Reward/risk index -94,3% -48,4% -100,0% -19,9% -100,0% -5,8% -100,0% -50,4% -80,4% 12,9% -98,8% -92,5% -100,0% -81,3% -64,7% -97,1% -42,5%
Sharpe ratio -0,36 -0,35 -0,35 -1,56 -0,39 -0,20 -0,63 -0,30 -0,39 -0,06 -0,53 -0,56 -0,31 -0,47 -0,11 -0,58 -0,24
Total trades 4 3 7 9 2 1 2 1 47 40 181 186 218 228 232 - -
Avg. Profit/Avg. Loss 0,51 - 0,28 1,07 - - - - 1,65 1,89 1,49 1,38 1,59 1,65 1,53 - -
Profitable trades 50,0% 0,0% 28,6% 33,3% 0,0% 0,0% 0,0% 0,0% 29,8% 35,0% 33,1% 36,0% 34,9% 33,8% 33,2% - -
89
Appendix 54: Trading rule results for Australia ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Australia In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -1,0% 31,6% -0,6% -2,0% -12,3% 0,8% -19,5% -5,1% -20,1% 9,8% 18,4% -20,0% -1,5% -40,4% -44,5% -21,3% 1,7%
Annualized performance -0,2% 6,3% -0,1% -8,6% -3,0% 0,2% -4,9% -1,2% -5,0% 2,1% 4,0% -4,9% -0,3% -11,0% -12,4% -5,4% 0,4%
Highest open drawdown
(HOD) 9,5% 14,8% 17, % 13,2% 12,3% 8,8% 19,9% 6,2% 21,9% 17,1% 4,2% 27,1% 8,3% 42,2% 81,9% 28,0% 31,4%
Standard deviation of daily
returns 1,3% 1,5% 0,8% 0,8% 0,5% 0,8% 0,6% 0,7% 0,9% 1,0% 0,9% 1,1% 0,9% 1,1% 6,2% 1,4% 1,7%
Buy and hold index 25,8% 29,4% 26,3% -3,7% 11,4% -0,9% 2,3% -6,7% 1,5% 8,0% 50,4% -21,3% 25,1% -41,4% -45,4% 0,0% 0,0%
Profit/loss index -2,8 74,2 -2,9 -45,1 -100,0 10,4 -100,0 -56,0 -25,8 10,5 5,8 -7,3 -0,4 -15,7 -1,3 - -
Reward/risk index -10,6% 68,0% -3,5% -15,1% -100,0% 8,1% -98,3% -82,7% -91,6% 36,6% 81,4% -73,8% -18,2% -95,8% -54,3% -76,0% 5,2%
Sharpe ratio -0,01 0,22 -0,01 -0,78 -0,39 -0,09 -0,54 -0,21 -0,37 0,06 0,27 -0,33 -0,02 -0,70 -0,14 -0,25 -0,03
Total trades 4 4 6 9 2 2 2 2 48 53 163 183 227 238 198 - -
Avg. Profit/Avg. Loss 0,42 1,64 1,05 1,21 - 1,20 - 0,48 1,26 2,42 1,68 1,43 1,95 1,30 1,44 - -
Profitable trades 75,0% 75,0% 50,0% 33,3% 0,0% 50,0% 0,0% 50,0% 37,5% 34,0% 41,1% 38,3% 34,4% 36,1% 38,9% - -
.
90
Appendix 55: Trading rule results for Vietnam ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Vietnam In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 15,8% -13,2% -10,5% 1,4% -34,2% -15,3% -36,5% -15,1% 47,6% 4,8% -11,4% -11,0% 12,4% -14,3% -19,0% -24,3% -8,1%
Annualized performance 3,4% -3,1% -2,5% 6,4% -9,2% -3,6% -9,9% -3,6% 9,4% 1,1% -2,8% -2,6% 2,7% -3,4% -4,6% -6,2% -1,9%
Highest open drawdown
(HOD) 7,5% 38,8% 10,5% 6,9% 34,7% 15,3% 41,7% 15,1% 16,2% 20,7% 21,4% 18,3% 19,0% 17,0% 127,1% 26,9% 35,2%
Standard deviation of daily
returns 1,1% 1,3% 1,1% 0,8% 1,2% 0,9% 1,2% 0,8% 1,1% 0,9% 1,1% 1,0% 1,1% 0,9% 38,0% 1,7% 1,5%
Buy and hold index 53,0% -5,5% 18,3% 10,3% -13,0% -7,8% -16,1% -7,6% 95,0% 14,1% 17,0% -3,1% 48,5% -6,7% -11,9% 0,0% 0,0%
Profit/loss index 35,9 -94,7 -33,2 29,2 -100,0 -66,5 -100,0 -100,0 27,0 4,8 -2,4 -3,4 2,6 -4,4 -2,0 - -
Reward/risk index 67,8% -34,0% -100,0% 16,8% -98,6% -99,8% -87,6% -100,0% 74,6% 18,8% -53,3% -60,2% 39,5% -83,8% -15,0% -90,5% -23,1%
Sharpe ratio 0,19 -0,22 -0,15 0,39 -0,48 -0,36 -0,53 -0,36 0,54 -0,01 -0,16 -0,24 0,16 -0,31 -0,01 -0,24 -0,13
Total trades 7 3 8 7 3 3 2 2 39 50 171 185 211 215 275 - -
Avg. Profit/Avg. Loss 1,40 0,12 2,41 1,21 - 0,82 - - 2,80 2,69 2,22 1,71 2,20 2,00 2,06 - -
Profitable trades 57,1% 33,3% 25,0% 57,1% 0,0% 33,3% 0,0% 0,0% 38,5% 30,0% 30,4% 35,7% 33,6% 31,6% 32,0% - -
91
Appendix 56: Trading rule results for Hong Kong ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV Kelly criterion Buy-and-hold
Hong Kong In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 41,2% 10,9% 11,9% 1,0% -2,4% 5,0% 6,1% -7,7% 21,3% 24,6% 8,5% 3,7% 9,9% 0,2% 63,7% 13,2% 10,2%
Annualized performance 8,3% 2,3% 2,6% 4,5% -0,6% 1,1% 1,4% -1,8% 4,6% 5,1% 1,9% 0,8% 2,2% 0,0% 11,7% 2,9% 2,2%
Highest open drawdown (HOD) 11,0% 10,0% 0,1% 4,2% 5,2% 5,6% 1,7% 9,3% 1,7% 6,1% 5,2% 8,4% 13,1% 13,9% 34,1% 20,8% 13,0%
Standard deviation of daily returns 0,9% 1,0% 0,6% 0,7% 0,7% 0,7% 0,8% 0,8% 0,8% 0,8% 0,8% 0,9% 0,8% 0,9% 2,9% 1,2% 1,2%
Buy and hold index 24,7% 0,6% -1,1% -8,4% -13,8% -4,7% -6,3% -16,3% 7,1% 13,1% -4,2% -5,9% -2,9% -9,1% 48,5% 0,0% 0,0%
Profit/loss index 87,8 91,7 50,5 17,5 -100,0 30,0 6,1 -43,6 20,1 28,5 3,3 1,2 3,6 0,1 22,6 - -
Reward/risk index 78,9% 52,2% 99,5% 19,0% -45,8% 47,3% 78,3% -83,5% 92,6% 80,1% 61,7% 30,6% 43,0% 1,3% 65,1% 38,8% 44,0%
Sharpe ratio 0,57 0,07 0,26 0,32 -0,05 -0,01 0,10 -0,25 0,38 0,32 0,14 -0,03 0,17 -0,08 0,23 0,15 0,05
Total trades 6 5 6 7 2 2 1 2 44 42 179 183 216 216 54 - -
Avg. Profit/Avg. Loss 2,06 3,24 1,17 1,79 - 1,67 - 0,69 1,71 1,62 1,85 1,49 2,04 1,75 1,62 - -
Profitable trades 83,3% 80,0% 66,7% 42,9% 0,0% 50,0% 100,0% 50,0% 45,5% 50,0% 36,9% 41,0% 35,2% 37,0% 51,9% - -
92
Appendix 57: Trading rule results for United Kingdom ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
United Kingdom In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 17,9% 12,7% -18,3% -3,3% -2,8% -10,2% 9,9% -13,8% 3,8% 8,6% 30,5% -19,1% 35,8% -31,7% -241,5% -0,9% -18,5%
Annualized performance 3,9% 2,7% -4,6% -13,8% -0,7% -2,4% 2,2% -3,3% 0,9% 1,9% 6,3% -4,7% 7,3% -8,2% - -0,2% -4,5%
Highest open drawdown
(HOD) 3,3% 21,8% 18,3% 16,5% 5,5% 10,2% 5,5% 13,8% 9,5% 17,7% 4,0% 21,6% 1,9% 36,0% 269,4% 11,1% 38,1%
Standard deviation of daily
returns 0,7% 1,3% 0,8% 0,7% 0,6% 0,5% 0,5% 0,5% 0,7% 0,8% 0,8% 1,0% 0,8% 1,0% 30,8% 1,1% 1,4%
Buy and hold index 19,0% 38,2% -17,6% 18,7% -1,9% 10,2% 10,8% 5,7% 4,7% 33,2% 31,7% -0,8% 37,0% -16,2% -273,5% 0,0% 0,0%
Profit/loss index 45,4 37,4 -92,8 -63,6 -22,2 -86,6 9,9 -98,2 4,6 11,6 12,7 -9,4 12,9 -13,7 -0,4 - -
Reward/risk index 84,5% 36,8% -100,0% -19,8% -50,6% -100,0% 64,2% -100,0% 28,7% 32,8% 88,4% -88,7% 95,0% -88,0% -89,6% -7,8% -48,5%
Sharpe ratio 0,33 0,08 -0,36 -1,35 -0,07 -0,46 0,25 -0,59 0,08 0,05 0,53 -0,38 0,58 -0,62 - -0,01 -0,26
Total trades 5 5 8 7 2 2 1 2 42 41 167 174 196 242 423 - -
Avg. Profit/Avg. Loss 1,46 1,24 0,58 1,07 0,89 0,15 - 0,02 1,42 1,93 2,36 1,17 2,11 1,31 1,04 - -
Profitable trades 60,0% 60,0% 12,5% 28,6% 50,0% 50,0% 100,0% 50,0% 42,9% 39,0% 35,3% 42,5% 38,3% 37,2% 39,7% - -
93
Appendix 58: Trading rule results for Peru ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Peru In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -30,4% 0,6% -5,6% 8,7% -26,7% 22,8% -30,6% 22,2% 6,4% 152,8% 3,3% 116,0% 26,0% 100,1% 1124,3% -51,9% 42,0%
Annualized performance -8,0% 0,1% -1,3% 45,3% -6,9% 4,7% -8,1% 4,6% 1,4% 23,1% 0,8% 18,8% 5,5% 16,8% 75,3% -15,5% 8,2%
Highest open drawdown
(HOD) 30,9% 26,5% 5,6% 0,0% 26,7% 7,1% 30,6% 5,5% 0,4% 0,0% 5,3% 5,5% 6,9% 0,4% 2,6% 52,2% 10,2%
Standard deviation of
daily returns 1,0% 1,1% 0,5% 0,9% 0,5% 0,9% 0,6% 0,8% 0,8% 0,8% 0,8% 1,1% 0,8% 1,0% 2,4% 1,2% 1,4%
Buy and hold index 44,8% -29,2% 96,4% -23,4% 52,4% -13,5% 44,5% -13,9% 121,4% 78,1% 114,9% 52,2% 162,2% 40,9% 762,3% 0,0% 0,0%
Profit/loss index -68,4 1,7 -40,9 62,1 -100,0 54,5 -100,0 22,2 7,5 61,9 1,5 19,2 9,7 17,3 0,0 - -
Reward/risk index -98,5% 2,1% -100,0% 100,0% -100,0% 76,2% -100,0% 80,1% 94,6% 100,0% 38,5% 95,5% 79,0% 99,6% 99,8% -99,4% 80,4%
Sharpe ratio -0,49 -0,06 -0,17 2,99 -0,85 0,24 -0,87 0,27 0,11 1,65 0,06 1,05 0,41 0,98 1,95 -0,83 0,31
Total trades 6 6 5 7 3 2 2 1 31 36 164 183 180 184 220 - -
Avg. Profit/Avg. Loss 0,45 1,15 0,43 2,84 - 3,11 - - 2,14 3,11 2,46 2,13 2,71 2,08 2,26 - -
Profitable trades 50,0% 50,0% 60,0% 57,1% 0,0% 50,0% 0,0% 100,0% 35,5% 58,3% 29,9% 44,8% 31,7% 44,0% 46,4% - -
94
Appendix 59: Trading rule results for Norway ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Norway In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 5,9% 1,1% -20,8% -3,4% -11,0% 1,5% -13,7% 14,5% -7,7% 15,5% -14,6% -2,7% -4,8% -30,9% 21,5% -31,3% -1,7%
Annualized performance 1,3% 0,3% -5,2% -14,4% -2,6% 0,3% -3,3% 3,1% -1,8% 3,3% -3,6% -0,6% -1,1% -8,0% 4,5% -8,3% -0,4%
Highest open drawdown
(HOD) 6,9% 32,4% 21,1% 16,4% 13,2% 12,3% 13,7% 4,1% 12,3% 13,4% 21,7% 19,0% 17,7% 46,0% 373,5% 31,3% 32,7%
Standard deviation of daily
returns 1,1% 1,4% 1,1% 0,9% 0,7% 0,9% 0,7% 0,7% 1,1% 1,0% 1,1% 1,1% 1,1% 1,1% 214,0% 1,6% 1,6%
Buy and hold index 54,1% 2,9% 15,3% -1,7% 29,5% 3,3% 25,6% 16,5% 34,3% 17,6% 24,3% -1,0% 38,5% -29,7% 23,6% 0,0% 0,0%
Profit/loss index 9,9 4,7 -76,9 -51,2 -100,0 6,9 -100,0 14,5 -6,5 13,4 -4,3 -0,7 -1,2 -9,7 -82,8 - -
Reward/risk index 45,9% 3,3% -98,4% -20,8% -82,8% 11,0% -100,0% 78,0% -62,8% 53,8% -67,3% -14,2% -27,1% -67,3% 5,4% -100,0% -5,3%
Sharpe ratio 0,08 -0,04 -0,31 -1,08 -0,23 -0,06 -0,29 0,17 -0,11 0,13 -0,20 -0,10 -0,07 -0,51 0,00 -0,33 -0,06
Total trades 5 3 7 9 2 2 2 1 38 43 184 178 171 188 193 - -
Avg. Profit/Avg. Loss 0,91 2,52 0,69 1,14 - 1,31 - - 1,67 1,84 1,51 1,71 2,01 1,53 2,20 - -
Profitable trades 60,0% 33,3% 28,6% 33,3% 0,0% 50,0% 0,0% 100,0% 36,8% 41,9% 38,0% 37,1% 33,3% 35,1% 35,2% - -
95
Appendix 60: Trading rule results for Spain ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Spain In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -15,7% -12,3% 2,9% -2,9% 24,4% 4,5% 18,2% 13,8% 18,1% -15,3% -5,8% -10,4% -43,5% -13,8% -28,5% -19,4% -19,7%
Annualized performance -3,9% -2,9% 0,7% -12,3% 5,2% 1,0% 3,9% 2,9% 3,9% -3,7% -1,4% -2,4% -12,4% -3,3% -7,3% -4,9% -4,8%
Highest open drawdown
(HOD) 27,8% 32,8% 23,4% 16,7% 9,2% 0,3% 11,2% 0,5% 30,1% 27,1% 31,2% 20,0% 46,2% 26,8% 78,2% 43,1% 35,1%
Standard deviation of daily
returns 1,2% 1,4% 1,0% 0,7% 0,8% 0,5% 0,9% 0,5% 1,2% 1,0% 1,2% 1,1% 1,2% 1,2% 6,6% 1,7% 1,6%
Buy and hold index 4,6% 9,1% 27,7% 20,9% 54,4% 30,2% 46,7% 41,7% 46,6% 5,5% 16,9% 11,5% -29,9% 7,4% -11,0% 0,0% 0,0%
Profit/loss index -59,5 -61,8 5,8 -44,2 24,4 28,0 18,2 13,8 11,7 -24,4 -1,1 -2,9 -9,5 -3,6 -0,4 - -
Reward/risk index -56,6% -37,7% 10,9% -17,3% 72,6% 94,1% 61,9% 96,6% 37,5% -56,3% -18,6% -52,1% -94,3% -51,3% -36,5% -45,1% -56,1%
Sharpe ratio -0,20 -0,19 0,04 -1,16 0,39 -0,02 0,29 0,23 0,21 -0,31 -0,07 -0,20 -0,63 -0,24 -0,08 -0,18 -0,24
Total trades 3 4 5 8 1 2 1 1 39 43 177 177 223 214 222 - -
Avg. Profit/Avg. Loss 0,27 0,44 2,00 2,00 - 1,61 - - 1,69 1,29 1,81 1,60 1,82 1,77 1,57 - -
Profitable trades 66,7% 50,0% 40,0% 25,0% 100,0% 50,0% 100,0% 100,0% 43,6% 37,2% 35,6% 37,3% 30,0% 34,6% 37,4% - -
96
Appendix 61: Trading rule results for Singapore ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Singapore In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 7,3% 13,2% -16,8% -4,0% -21,8% 4,7% -13,6% -4,6% 32,8% 29,0% -4,6% -4,3% -11,8% 29,4% 182,0% -20,1% -6,5%
Annualized performance 1,6% 2,8% -4,2% -16,5% -5,5% 1,0% -3,3% -1,0% 6,8% 5,9% -1,1% -1,0% -2,9% 6,0% 26,2% -5,0% -1,5%
Highest open drawdown
(HOD) 3,6% 14,6% 16,8% 18,1% 21,8% 1,3% 13,6% 4,6% 5,4% 3,7% 9,2% 12,3% 21,4% 8,7% 43,3% 22,5% 23,9%
Standard deviation of daily
returns 1,0% 1,1% 0,5% 0,6% 0,6% 0,5% 0,5% 0,6% 0,7% 0,8% 0,8% 0,9% 0,8% 0,8% 3,7% 1,1% 1,2%
Buy and hold index 34,3% 21,0% 4,1% 2,7% -2,2% 11,9% 8,1% 2,0% 66,1% 38,0% 19,3% 2,3% 10,3% 38,4% 201,5% 0,0% 0,0%
Profit/loss index 21,3 39,2 -79,5 -58,5 -100,0 34,7 -100,0 -44,7 35,9 32,4 -2,2 -1,4 -6,0 8,5 0,4 - -
Reward/risk index 67,0% 47,4% -100,0% -21,9% -100,0% 78,3% -100,0% -100,0% 85,9% 88,6% -49,9% -34,9% -55,1% 77,1% 80,8% -89,3% -27,0%
Sharpe ratio 0,11 0,09 -0,49 -1,74 -0,59 -0,02 -0,43 -0,24 0,60 0,38 -0,09 -0,15 -0,24 0,37 0,43 -0,29 -0,14
Total trades 6 6 8 8 3 2 2 2 33 42 179 189 222 211 239 - -
Avg. Profit/Avg. Loss 0,77 0,96 0,72 1,52 - 1,74 - 0,62 2,55 2,10 2,13 1,77 2,37 1,89 2,13 - -
Profitable trades 66,7% 66,7% 25,0% 25,0% 0,0% 50,0% 0,0% 50,0% 42,4% 45,2% 31,3% 36,0% 27,5% 39,3% 38,5% - -
97
Appendix 62: Trading rule results for Philippines ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Philippines In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -0,2% -3,7% 54,1% -0,1% 23,0% -19,7% 11,0% -27,5% 20,1% -1,1% 13,6% 17,7% 13,2% 5,9% -11,9% 36,3% -17,9%
Annualized performance -0,1% -0,8% 10,5% -0,5% 4,9% -4,8% 2,4% -7,0% 4,3% -0,2% 3,0% 3,7% 2,9% 1,3% -2,8% 7,4% -4,3%
Highest open drawdown
(HOD) 8,8% 34,2% 0,3% 8,9% 1,1% 19,9% 1,1% 27,5% 1,1% 15,4% 13,2% 10,9% 6,7% 16,3% 110,0% 15,0% 42,5%
Standard deviation of daily
returns 1,2% 1,5% 0,8% 0,8% 0,9% 0,8% 1,0% 0,8% 0,9% 0,9% 1,0% 1,2% 1,0% 1,0% 39,1% 1,4% 1,7%
Buy and hold index -26,8% 17,3% 13,1% 21,7% -9,8% -2,2% -18,6% -11,7% -11,9% 20,5% -16,7% 43,4% -17,0% 29,0% 7,4% 0,0% 0,0%
Profit/loss index -0,7 -13,9 54,1 -2,8 23,0 -100,0 11,0 -100,0 19,6 -1,0 3,8 4,3 3,2 1,7 -4,0 - -
Reward/risk index -2,5% -10,9% 99,5% -1,3% 95,6% -99,0% 91,3% -100,0% 94,7% -6,9% 50,7% 61,8% 66,3% 26,4% -10,8% 70,8% -42,2%
Sharpe ratio 0,00 -0,09 0,87 -0,13 0,34 -0,45 0,15 -0,67 0,31 -0,10 0,20 0,13 0,19 0,01 -0,01 0,34 -0,21
Total trades 4 5 4 8 2 3 2 3 45 46 181 168 213 226 230 - -
Avg. Profit/Avg. Loss 1,13 1,46 - 1,71 - - - - 1,93 1,48 1,54 2,05 1,78 2,27 1,60 - -
Profitable trades 50,0% 40,0% 100,0% 37,5% 100,0% 0,0% 100,0% 0,0% 42,2% 41,3% 42,0% 36,3% 38,5% 32,3% 37,0% - -
98
Appendix 63: Trading rule results for Austria ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Austria In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 21,1% -7,4% 2,9% 9,9% -14,7% 22,1% 10,7% 39,0% 11,6% 5,1% 25,8% 16,1% -1,5% 20,3% 314,3% -14,4% -2,5%
Annualized performance 4,5% -1,7% 0,7% 52,6% -3,6% 4,6% 2,4% 7,7% 2,6% 1,1% 5,4% 3,4% -0,4% 4,2% 37,5% -3,5% -0,6%
Highest open drawdown (HOD) 10,4% 39,4% 7,8% 5,4% 14,7% 3,3% 4,7% 3,3% 15,6% 14,7% 17,2% 9,9% 22,9% 16,6% 32,2% 28,8% 31,0%
Standard deviation of daily
returns 1,2% 1,3% 0,8% 0,8% 0,7% 0,6% 0,6% 0,6% 1,0% 0,9% 1,0% 1,1% 1,1% 1,0% 3,1% 1,5% 1,5%
Buy and hold index 41,6% -5,0% 20,3% 12,8% -0,3% 25,2% 29,4% 42,6% 30,4% 7,8% 47,1% 19,2% 15,1% 23,4% 325,0% 0,0% 0,0%
Profit/loss index 66,2 -28,5 13,2 79,5 -91,4 52,1 10,7 39,0 11,0 4,5 7,5 3,7 -0,5 6,0 1,2 - -
Reward/risk index 66,9% -18,8% 27,2% 64,7% -100,0% 87,0% 69,7% 92,2% 42,7% 25,7% 60,1% 62,0% -6,6% 54,9% 90,7% -50,2% -8,1%
Sharpe ratio 0,25 -0,15 0,05 4,28 -0,31 0,35 0,23 0,68 0,15 -0,01 0,33 0,13 -0,02 0,19 0,75 -0,15 -0,07
Total trades 5 4 6 5 3 2 1 1 37 46 164 180 173 177 236 - -
Avg. Profit/Avg. Loss 0,89 0,87 2,55 5,08 0,20 2,97 - - 1,84 1,77 1,55 1,56 1,73 2,06 2,34 - -
Profitable trades 80,0% 50,0% 33,3% 60,0% 33,3% 50,0% 100,0% 100,0% 40,5% 39,1% 43,9% 42,2% 37,0% 36,2% 42,4% - -
99
Appendix 64: Trading rule results for Thailand ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Thailand In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 11,9% 31,3% 27,0% 5,8% -4,6% 13,5% -12,6% 8,6% 28,7% 55,4% -9,0% -2,3% -12,7% -5,9% 267,4% -11,8% 23,8%
Annualized performance 2,6% 6,3% 5,7% 28,5% -1,1% 2,9% -3,0% 1,9% 6,0% 10,4% -2,2% -0,5% -3,1% -1,4% 33,9% -2,9% 4,9%
Highest open drawdown (HOD) 11,5% 12,2% 3,6% 0,9% 11,0% 5,0% 12,8% 5,1% 4,4% 2,9% 18,2% 11,1% 13,1% 11,4% 8,6% 24,2% 14,9%
Standard deviation of daily
returns 1,3% 1,3% 0,8% 0,8% 0,9% 0,7% 1,0% 0,7% 1,0% 0,9% 1,1% 1,0% 1,1% 1,0% 2,7% 1,5% 1,5%
Buy and hold index 26,9% 6,0% 43,9% -14,6% 8,2% -8,4% -0,9% -12,3% 45,8% 25,5% 3,1% -21,1% -1,0% -24,0% 196,7% 0,0% 0,0%
Profit/loss index 22,3 52,7 50,3 64,1 -87,6 58,1 -100,0 42,9 30,2 39,4 -2,3 -0,7 -3,1 -2,0 16,2 - -
Reward/risk index 50,8% 71,9% 88,3% 86,4% -41,6% 72,8% -98,1% 62,9% 86,7% 95,0% -49,7% -20,6% -96,4% -52,0% 96,9% -48,8% 61,6%
Sharpe ratio 0,13 0,25 0,45 2,19 -0,08 0,15 -0,19 0,06 0,38 0,68 -0,12 -0,11 -0,18 -0,17 0,76 -0,12 0,16
Total trades 6 5 5 6 2 2 2 2 38 43 178 194 227 237 56 - -
Avg. Profit/Avg. Loss 0,81 0,71 1,70 3,55 0,13 2,94 - 2,10 1,52 3,32 1,63 1,84 1,98 1,81 3,40 - -
Profitable trades 66,7% 80,0% 60,0% 50,0% 50,0% 50,0% 0,0% 50,0% 52,6% 39,5% 37,6% 35,6% 32,6% 35,0% 44,6% - -
100
Appendix 65: Trading rule results for Mexico ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Mexico In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 5,7% 24,3% -20,4% -5,9% -9,2% -23,9% -22,4% -32,7% -8,9% 8,8% 15,5% -24,1% -12,3% -24,8% -456,8% -15,1% -32,6%
Annualized performance 1,3% 5,0% -5,1% -23,8% -2,2% -5,9% -5,7% -8,5% -2,1% 1,9% 3,4% -6,0% -3,0% -6,2% - -3,7% -8,5%
Highest open drawdown
(HOD) 0,0% 13,9% 20,4% 29,6% 12,6% 23,9% 22,4% 32,7% 8,9% 21,3% 10,3% 27,3% 20,5% 31,9% 529,5% 19,5% 48,8%
Standard deviation of
daily returns 1,1% 1,6% 0,8% 1,0% 0,7% 0,6% 0,7% 0,7% 0,9% 1,2% 0,9% 1,3% 0,9% 1,2% 19,7% 1,3% 1,8%
Buy and hold index 24,5% 84,4% -6,3% 39,6% 6,9% 12,9% -8,6% -0,2% 7,2% 61,5% 36,0% 12,6% 3,2% 11,6% -629,4% 0,0% 0,0%
Profit/loss index 14,6 50,0 -61,3 -62,4 -95,9 -100,0 -100,0 -100,0 -10,0 6,2 5,3 -6,9 -4,1 -6,2 -0,5 - -
Reward/risk index 100,0% 63,6% -100,0% -20,0% -73,1% -100,0% -100,0% -100,0% -100,0% 29,3% 60,1% -88,2% -60,1% -77,8% -86,3% -77,3% -66,8%
Sharpe ratio 0,07 0,15 -0,41 -1,61 -0,21 -0,80 -0,53 -0,87 -0,15 0,04 0,24 -0,35 -0,22 -0,40 - -0,18 -0,34
Total trades 4 7 8 9 2 2 2 2 47 41 177 184 244 245 193 - -
Avg. Profit/Avg. Loss 1,51 1,00 0,80 1,72 0,05 - - - 1,62 2,30 1,72 1,46 1,99 1,78 0,90 - -
Profitable trades 50,0% 71,4% 37,5% 22,2% 50,0% 0,0% 0,0% 0,0% 36,2% 34,1% 40,1% 37,5% 32,0% 33,1% 38,3% - -
101
Appendix 66: Trading rule results for Egypt ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Egypt In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -30,9% -23,1% 59,9% -0,1% 7,5% -20,2% -12,1% 4,8% 112,1% 19,3% -16,3% -52,2% 19,0% 4,7% 34,3% 1,5% -39,0%
Annualized performance -8,2% -5,7% 11,4% -0,3% 1,7% -4,9% -2,9% 1,0% 18,9% 4,0% -4,0% -15,3% 4,1% 1,0% 6,8% 0,3% -10,5%
Highest open drawdown (HOD) 36,3% 42,5% 8,8% 15,0% 18,1% 25,4% 28,6% 2,1% 5,6% 8,8% 18,3% 58,2% 5,6% 17,9% 111,2% 32,8% 54,4%
Standard deviation of daily
returns 1,6% 1,9% 1,0% 1,4% 0,9% 0,9% 1,0% 0,7% 1,2% 1,9% 1,2% 1,3% 1,3% 1,2% 107,4% 1,8% 1,7%
Buy and hold index -31,9% 26,1% 57,5% 63,9% 5,9% 31,0% -13,4% 71,8% 109,0% 95,7% -17,5% -21,6% 17,2% 71,8% 120,3% 0,0% 0,0%
Profit/loss index -68,4 -63,3 93,9 -1,1 45,4 -81,9 -49,2 4,8 35,5 15,5 -3,2 -19,2 3,1 1,4 -4,1 - -
Reward/risk index -85,2% -54,5% 87,2% -0,4% 29,4% -79,5% -42,4% 69,8% 95,2% 68,8% -89,1% -89,7% 77,3% 20,8% 23,6% 4,4% -71,8%
Sharpe ratio -0,33 -0,24 0,72 -0,07 0,11 -0,44 -0,19 -0,01 0,99 0,09 -0,21 -0,81 0,20 -0,01 0,00 0,01 -0,43
Total trades 5 5 3 6 2 2 2 1 37 40 180 186 160 170 218 - -
Avg. Profit/Avg. Loss 0,79 0,74 12,45 2,27 1,74 0,24 0,67 - 3,51 2,57 1,90 1,59 2,15 1,84 1,96 - -
Profitable trades 40,0% 40,0% 66,7% 33,3% 50,0% 50,0% 50,0% 100,0% 40,5% 35,0% 33,3% 29,6% 34,4% 36,5% 36,2% - -
102
Appendix 67: Trading rule results for Indonesia ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Indonesia In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -19,7% -21,0% -20,2% -2,4% -16,8% -0,9% -26,8% 6,1% -34,9% 42,1% 32,0% 16,5% 29,6% 14,3% 52,2% -42,5% -8,6%
Annualized performance -4,9% -5,2% -5,1% -10,2% -4,2% -0,2% -7,0% 1,3% -9,4% 8,2% 6,6% 3,5% 6,2% 3,0% 9,9% -12,0% -2,0%
Highest open drawdown
(HOD) 38,6% 50,5% 24,6% 18,1% 17,7% 9,1% 26,8% 7,6% 40,7% 21,6% 7,4% 3,6% 9,0% 15,3% 34,0% 52,6% 41,8%
Standard deviation of daily
returns 1,6% 1,5% 0,9% 1,0% 0,6% 1,0% 0,6% 0,7% 1,2% 1,1% 1,2% 1,2% 1,2% 1,2% 3,7% 1,8% 1,8%
Buy and hold index 39,8% -13,6% 38,8% 6,8% 44,8% 8,5% 27,3% 16,1% 13,3% 55,5% 129,8% 27,5% 125,5% 25,1% 66,5% 0,0% 0,0%
Profit/loss index -64,8 -77,4 -68,9 -45,9 -100,0 -8,4 -100,0 6,1 -35,4 31,0 6,9 3,3 5,7 2,9 0,0 - -
Reward/risk index -50,9% -41,7% -82,4% -13,1% -94,9% -9,8% -100,0% 44,4% -85,7% 66,0% 81,2% 82,1% 76,7% 48,3% 60,6% -80,8% -20,6%
Sharpe ratio -0,19 -0,27 -0,38 -0,74 -0,47 -0,09 -0,71 0,01 -0,50 0,39 0,34 0,12 0,33 0,10 0,15 -0,42 -0,11
Total trades 3 2 7 8 2 2 2 1 49 44 171 178 199 186 366 - -
Avg. Profit/Avg. Loss 0,93 0,31 0,94 1,86 - 1,02 - - 1,50 2,83 1,86 1,89 2,13 1,80 1,69 - -
Profitable trades 33,3% 50,0% 28,6% 25,0% 0,0% 50,0% 0,0% 100,0% 30,6% 38,6% 39,8% 37,1% 36,2% 38,7% 38,0% - -
103
Appendix 68: Trading rule results for Brazil ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Brazil In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -26,1% 30,5% -25,1% 2,0% -19,5% 25,6% -26,6% 8,1% -38,8% 164,0% -2,3% -70,9% -44,6% -44,9% -174,8% -68,6% 48,9%
Annualized performance -6,7% 6,1% -6,4% 9,0% -4,9% 5,2% -6,9% 1,8% -10,7% 24,3% -0,5% -24,2% -12,7% -12,5% - -23,5% 9,3%
Highest open drawdown
(HOD) 26,2% 10,6% 26,7% 1,2% 19,5% 6,9% 26,6% 3,8% 38,8% 0,0% 8,3% 76,0% 44,9% 54,6% 202,7% 68,7% 13,1%
Standard deviation of
daily returns 1,6% 1,9% 0,9% 1,6% 0,6% 1,6% 0,8% 1,5% 1,3% 1,7% 1,3% 1,8% 1,2% 1,8% 17,7% 1,8% 2,6%
Buy and hold index 135,6% -12,4% 138,8% -31,6% 156,7% -15,7% 133,9% -27,4% 95,1% 77,2% 211,5% -80,4% 76,6% -63,0% -150,2% 0,0% 0,0%
Profit/loss index -55,6 41,6 -79,6 16,6 -100,0 65,6 -100,0 34,4 -29,4 28,3 -0,4 -7,2 -10,2 -3,1 -0,2 - -
Reward/risk index -99,5% 74,2% -94,1% 62,7% -100,0% 78,8% -100,0% 68,0% -100,0% 100,0% -27,4% -93,3% -99,4% -82,1% -86,2% -99,9% 78,9%
Sharpe ratio -0,27 0,16 -0,46 0,32 -0,50 0,16 -0,58 0,02 -0,53 0,88 -0,03 -0,88 -0,66 -0,48 - -0,80 0,20
Total trades 6 4 7 10 1 2 1 2 42 38 168 208 233 251 214 - -
Avg. Profit/Avg. Loss 1,29 0,82 1,56 3,48 - 4,04 - 1,88 1,94 3,18 1,73 1,31 1,94 1,75 1,32 - -
Profitable trades 33,3% 75,0% 14,3% 30,0% 0,0% 50,0% 0,0% 50,0% 26,2% 42,1% 36,9% 35,1% 28,3% 33,1% 36,0% - -
104
Appendix 69: Trading rule results for India ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
India In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 79,6% 30,5% -39,8% -1,6% -1,3% 6,3% -7,3% 13,8% 8,4% -10,5% -43,5% -24,0% -67,5% -47,1% 139,7% -3,4% -11,8%
Annualized performance 14,5% 6,1% -11,1% -6,9% -0,3% 1,4% -1,7% 2,9% 1,9% -2,5% -12,3% -6,0% -22,9% -13,3% 21,7% -0,8% -2,8%
Highest open drawdown
(HOD) 3,6% 6,6% 39,8% 12,8% 15,5% 6,7% 17,7% 10,6% 11,7% 28,7% 46,3% 29,3% 68,1% 48,9% 142,6% 31,3% 36,8%
Standard deviation of daily
returns 1,2% 1,2% 1,0% 1,1% 0,9% 0,6% 0,9% 0,7% 1,1% 1,1% 1,2% 1,2% 1,2% 1,2 % 35,9% 1,7% 1,6%
Buy and hold index 85,9% 47,8% -37,7% 11,5% 2,2% 20,4% -4,0% 29,0% 12,3% 1,4% -41,5% -13,9% -66,4% -40,1% 171,7% 0,0% 0,0%
Profit/loss index 97,7 56,1 -81,7 -29,0 -10,0 29,3 -47,6 13,8 6,7 -11,2 -11,7 -7,1 -26,0 -16,6 -0,3 - -
Reward/risk index 95,7% 82,2% -100,0% -12,5% -8,5% 48,3% -41,0% 56,5% 41,7% -36,7% -93,9% -82,1% -99,1% -96,4% 49,5% -10,9% -31,9%
Sharpe ratio 0,75 0,26 -0,67 -0,48 -0,02 0,02 -0,12 0,16 0,10 -0,21 -0,64 -0,38 -1,25 -0,75 0,04 -0,03 -0,16
Total trades 5 6 9 8 2 2 2 1 44 46 189 184 241 212 238 - -
Avg. Profit/Avg. Loss 15,90 0,59 2,21 0,50 1,04 1,72 0,62 - 1,29 1,91 1,58 1,31 1,51 1,27 1,84 - -
Profitable trades 80,0% 83,3% 11,1% 62,5% 50,0% 50,0% 50,0% 100,0% 47,7% 32,6% 32,3% 40,2% 26,6% 35,4% 39,9% - -
.
105
Appendix 70: Trading rule results for South Africa ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
South Africa In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance 14,7% 48,9% -31,7% -9,2% -30,2% -15,1% -40,1% -2,8% -37,3% -29,7% -18,9% -13,1% -43,2% -37,5% 58,0% -32,1% -16,0%
Annualized performance 3,2% 9,3% -8,4% -34,8% -8,0% -3,6% -11,1% -0,6% -10,2% -7,6% -4,7% -3,1% -12,2% -10,0% 10,8% -8,5% -3,8%
Highest open drawdown
(HOD) 1,5% 7,8% 31,7% 35,4% 30,2% 15,4% 40,1% 18,6% 37,6% 47,1% 26,8% 26,1% 49,1% 43,1% 56,3% 38,0% 41,8%
Standard deviation of daily
returns 1,4% 1,7% 0,8% 1,2% 0,9% 1,2% 0,7% 1,1% 1,2% 1,4% 1,2% 1,6% 1,1% 1,5% 4,7% 1,7% 2,2%
Buy and hold index 68,9% 77,2% 0,7% 8,1% 2,8% 1,0% -11,7% 15,7% -7,7% -16,3% 19,5% 3,4% -16,4% -25,6% 88,0% 0,0% 0,0%
Profit/loss index 28,9 72,0 -92,2 -95,2 -100,0 -94,7 -100,0 -100,0 -37,6 -25,5 -4,4 -1,4 -13,5 -3,7 0,6 - -
Reward/risk index 90,7% 86,3% -100,0% -25,8% -100,0% -98,0% -100,0% -15,1% -99,1% -63,0% -70,5% -50,1% -88,0% -87,1% 50,7% -84,5% -38,2%
Sharpe ratio 0,14 0,30 -0,65 -1,87 -0,57 -0,25 -0,94 -0,10 -0,54 -0,39 -0,24 -0,17 -0,68 -0,46 0,13 -0,31 -0,14
Total trades 4 5 7 9 3 2 3 1 49 48 180 181 221 233 188 - -
Avg. Profit/Avg. Loss 0,62 1,28 0,62 0,51 - 0,06 - - 1,58 1,65 1,75 1,50 1,82 1,68 1,75 - -
Profitable trades 75,0% 80,0% 14,3% 11,1% 0,0% 50,0% 0,0% 0,0% 28,6% 31,3% 34,4% 39,8% 29,0% 34,3% 41,0% - -
106
Appendix 71: Trading rule results for Chile ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Chile In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -36,0% -25,0% -26,2% -2,3% -25,8% 23,4% -14,7% 9,8% -10,7% 60,7% 40,3% 39,9% -10,6% 104,6% 399,1% -52,6% -19,1%
Annualized performance -9,8% -6,3% -6,8% -9,9% -6,7% 4,8% -3,6% 2,1% -2,6% 11,2% 8,1% 7,8% -2,6% 17,4% 43,4% -15,8% -4,6%
Highest open drawdown
(HOD) 38,1% 52,4% 26,2% 10,0% 25,8% 6,1% 14,7% 5,5% 11,8% 0,0% 0,0% 4,0% 20,9% 2,7% 7,3% 54,2% 43,5%
Standard deviation of
daily returns 1,2% 1,4% 0,5% 0,8% 0,5% 0,9% 0,2% 0,8% 0,8% 0,9% 0,9% 1,2% 0,8% 1,1% 4,1% 1,3% 1,6%
Buy and hold index 35,0% -7,3 % 55,7% 20,8% 56,5% 52,5% 79,9% 35,8% 88,5% 98,6% 196,0% 72,9% 88,6% 152,9% 517,0% 0,0% 0,0%
Profit/loss index -82,8 -60,4 -93,1 -47,1 -100,0 23,4 -100,0 9,8 -11,5 32,4 12,0 5,4 -4,4 15,5 0,0 - -
Reward/risk index -94,4% -47,8% -100,0% -23,1% -100,0% 79,3% -100,0% 64,1% -90,4% 100,0% 100,0% 90,9% -50,8% 97,5% 98,2% -97,1% -43,9%
Sharpe ratio -0,51 -0,33 -0,78 -0,88 -0,82 0,26 -1,27 0,07 -0,19 0,67 0,56 0,34 -0,19 0,90 0,64 -0,77 -0,23
Total trades 5 4 7 8 2 1 1 1 40 39 160 174 196 191 213 - -
Avg. Profit/Avg. Loss 1,05 0,58 0,21 0,98 - - - - 2,13 2,26 2,03 1,83 1,86 2,25 1,73 - -
Profitable trades 20,0% 50,0% 28,6% 37,5% 0,0% 100,0% 0,0% 100,0% 30,0% 46,2% 39,4% 40,8% 33,7% 41,9% 41,8% - -
.
107
Appendix 72: Trading rule results for Colombia ETF in both in-sample (1st of September 2011 to 31st of December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods.
RSI TRB50 TRB150 TRB200 MACD STOCH-D OBV
Kelly
criterion Buy-and-hold
Colombia In Out In Out In Out In Out In Out In Out In Out Out In Out
Performance -56,2% -6,7% -2,6% -6,7% -36,0% -25,3% -10,2% -2,8% 16,9% 48,0% 27,6% 87,1% 19,6% 74,4% 800,5% -63,3% -18,7%
Annualized performance -17,3% -1,5% -0,6% -26,5% -9,8% -6,3% -2,5% -0,6% 3,7% 9,2% 5,8% 15,1% 4,2% 13,3% 63,7% -20,6% -4,5%
Highest open drawdown
(HOD) 60,9% 40,9% 2,6% 28,1% 36,0% 25,3% 11,4% 15,3% 4,3% 6,1% 8,0% 3,4% 9,4% 12,2% 45,1% 67,2% 45,1%
Standard deviation of
daily returns 1,2% 1,5% 0,5% 0,9% 0,6% 0,8% 0,4% 0,8% 0,9% 1,1% 1,0% 1,2% 0,9% 1,2% 5,8% 1,4% 1,8%
Buy and hold index 19,4% 14,8% 165,1% 14,8% 74,2% -8,1% 144,5% 19,6% 218,3% 82,1% 247,4% 130,2% 225,6% 114,6% 1008,0% 0,0% 0,0%
Profit/loss index -88,0 -16,2 -13,5 -76,9 -100,0 -95,2 -100,0 -100,0 13,8 26,4 7,0 12,3 6,3 11,4 0,0 - -
Reward/risk index -92,2% -16,4% -100,0% -23,8% -100,0% -100,0% -89,2% -18,1% 79,8% 88,7% 77,6% 96,2% 67,5% 86,0% 94,7% -94,1% -41,5%
Sharpe ratio -0,89 -0,12 -0,07 -1,91 -1,09 -0,57 -0,35 -0,15 0,26 0,48 0,38 0,72 0,31 0,62 0,68 -0,97 -0,20
Total trades 6 7 6 10 3 3 1 1 36 41 171 176 175 167 385 - -
Avg. Profit/Avg. Loss 0,48 0,40 1,93 1,19 - 0,12 - - 2,72 2,43 1,98 2,11 2,26 2,21 2,12 - -
Profitable trades 33,3% 71,4% 33,3% 20,0% 0,0% 33,3% 0,0% 0,0% 33,3% 41,5% 38,0% 41,5% 34,3% 38,9% 40,3% - -
108
Appendix 73: Fama-French 5-factor regression using monthly data for Kelly criterion. ***, **, * represent statistical
significance at 1%, 5%,10% level, respectively. Data is from the out-of-sample (4th of January 2016 to 19th of June 2020)
period.
Country a b s h r c
Taiwan 1,31 % 1,34*** 0,69 0,11 0,06 -0,03
China -0,67 % 1,48*** -2,64*** 0,30 -0,58 -3,13**
New Zealand 1,15 % 1,28*** 2,21* 0,09 1,24 0,23
Netherlands -0,29 % 2,49*** -2,46 3,22* 3,49 -4,34**
Switzerland -0,01 % 3,47*** -5,84*** -4,58** -4,27 2,24
Japan -0,05 % 2,15*** -0,41 3,09* 2,07 -4,88**
Sweden -2,28 % 3,26*** 0,14 4,60** 7,06*** 0,51
Israel -0,51 % 2,17*** 2,12 1,77 2,73 -2,49
Germany -1,87 % 3,27*** -2,23* 4,67*** 4,37** -2,53
South Korea -5,22 %* 5,02*** -1,63 -1,25 7,77** 2,03
Turkey -1,63 % 4,37*** 2,52 -2,44 -4,95 4,74
Belgium -2,78 %** 3,25*** 0,99 0,90 5,35*** 1,92
Ireland -4,09 % 4,28*** -0,69 6,05** 10,25*** -5,20
Canada 0,90 % 1,22*** 0,60 0,36 1,63 1,52
France 0,20 % 2,25*** -1,68* -0,09 1,57 2,78**
Italy -1,81 % 3,51*** -3,08* 3,82** 2,10 -2,63
Malaysia -3,16 %* 1,76*** -1,71 -1,36 -1,83 2,84
Australia -3,36 % 2,45*** 4,07** 7,50*** 9,68*** -0,38
Vietnam -3,68 %* 3,00*** 1,87 -0,83 3,57 4,29**
Hong Kong 0,31 % 1,68*** 0,47 -0,39 -1,46 -0,89
United Kingdom -5,06 %** 4,77*** -2,25 3,20 5,82* 4,55
Peru 4,32 %** 1,06** -1,07 -0,34 -2,56 1,28
Norway 2,03 % 5,25*** 7,68** -6,75* 0,55 20,95***
Spain -2,75 % 3,99*** -5,68** 5,45* 4,12 -4,99
Singapore 1,38% 2,21*** -1,03 -0,34 -1,05 1,22
Philippines -3,86 % 3,89*** 2,60 0,74 -1,14 0,87
Austria 2,52 % 1,75*** -1,10 -0,16 -1,29 0,64
Thailand 2,12 %* 1,65*** -0,42 -2,06** -1,32 3,60**
Mexico 1,20 % 3,21*** -2,89 -7,96** -5,73 5,72
Egypt -2,56 % 4,45** 0,19 -9,36 -1,15 4,76
Indonesia -0,19 % 1,28*** -0,33 -0,08 -1,56 -0,26
Brazil -3,67 % 3,52*** 0,95 -0,73 -0,10 3,33
India -0,33 % 3,98** -1,79 0,68 -5,96 1,13
South Africa 0,16 % 2,95*** 1,92 -0,77 -1,53 2,13
Chile 1,91 % 3,23*** 1,83 -0,90 1,32 5,88**
Colombia 2,17 % 2,42*** -3,01* -3,95** 3,13 5,37**
109
Appendix 74: Half Kelly portfolio weights for each individual country using different technical trading rules. The total
number of portfolios is 36. These are derived by using equation 14 and in-sample (1st of September 2011 to 31st of
December 2015) data to optimize.
Country MACD TRB50 TRB150 TRB200 RSI STOCH-D OBV Total
Taiwan 30,1 % - - 323,1 % 242,9 % - - 596,2 %
China 200,3 % 109,8 % - - 203,9 % - - 514,0 %
New Zealand 120,6 % 319,3 % - 222,9 % 115,1 % - 78,1 % 856,1 %
Netherlands 144,7 % - 642,2 % - 170,9 % - - 957,8 %
Switzerland - 375,5 % - - 91,9 % 443,1 % - 910,4 %
Japan 131,4 % 15,1 % - 360,4 % 301,3 % - 310,4 % 1118,5 %
Sweden - - 469,9 % - 131,0 % - 203,2 % 804,2 %
Israel 52,4 % - 50,3 % - 295,4 % 207,7 % - 605,8 %
Germany 14,4 % 116,8 % - 381,8 % 173,6 % 295,5 % - 982,1 %
South Korea - 151,4 % - - 227,2 % 428,2 % - 806,8 %
Turkey - - 54,2 % 85,5 % 130,5 % - 67,6 % 337,7 %
Belgium - - 157,8 % 49,3 % 292,2 % 103,0 % - 602,3 %
Ireland 1,1 % 445,6 % 638,8 % - 303,7 % - - 1389,2 %
Canada - 78,5 % - - 38,3 % 355,4 % - 472,2 %
France - - - 676,0 % 150,4 % 65,1 % - 891,4 %
Italy 166,1 % - - 243,4 % 71,2 % 79,6 % - 560,3 %
Malaysia - - 216,6 % - 133,4 % - 195,0 % 545,0 %
Australia - 56,6 % 66,8 % - 47,1 % 275,3 % - 445,8 %
Vietnam 242,9 % 90,9 % - - 201,8 % - 69,2 % 604,8 %
Hong Kong 80,0 % 269,2 % - - 293,1 % - - 642,2 %
United Kingdom - - - 912,0 % 48,2 % 237,8 % 240,2 % 1438,2 %
Peru 341,6 % - - - - - 601,8 % 943,4 %
Norway - - 2998,9 % - 99,5 % - 81,9 % 3180,3 %
Spain 247,0 % - 719,8 % - - 224,4 % - 1191,1 %
Singapore 636,7 % - - 89,6 % 55,5 % 141,6 % - 923,3 %
Philippines 33,9 % 575,6 % 64,4 % - 112,9 % 47,7 % - 834,4 %
Austria 65,9 % 78,1 % - 609,2 % 47,5 % 221,9 % - 1022,6 %
Thailand 150,6 % 345,8 % 123,8 % - 219,7 % - - 839,9 %
Mexico - - 1764,7 % - 187,8 % 332,7 % - 2285,2 %
Egypt 406,4 % 215,8 % 216,3 % - - - 2,3 % 840,8 %
Indonesia - - 87,6 % - - 166,5 % 177,8 % 431,8 %
Brazil - - 16,4 % - 42,7 % 270,9 % - 330,0 %
India 233,0 % - 1270,8 % - 396,3 % 150,0 % - 2050,1 %
South Africa - - 160,6 % - 200,0 % 118,9 % - 479,5 %
Chile 124,1 % - - - - 812,2 % - 936,4 %
Colombia 444,6 % - - 727,2 % - 585,6 % 130,5 % 1887,9 %
110
Appendix 75: Half Kelly portfolio weights for single technical indicator and 36 countries as possible assets. The total
number of portfolios is 7. These are derived by using equation 14 and in-sample (1st of September 2011 to 31st of
December 2015) data to optimize.
Country MACD TRB50 TRB150 TRB200 RSI STOCH-D OBV
Taiwan 51,7 % - - 114,1 % 76,5 % - -
China 225,4 % - - - 70,8 % - -
New Zealand 1,8 % 225,1 % 63,2 % 168,7 % - - -
Netherlands 325,1 % - - - 171,6 % - 145,0 %
Switzerland 434,0 % 731,2 % 561,2 % 106,9 % - 721,4 % 324,7 %
Japan 51,8 % - - - 69,4 % - 130,9 %
Sweden - - - - - - 170,3 %
Israel 23,7 % - 33,5 % - 170,1 % 59,8 % -
Germany - 564,7 % 249,2 % 288,2 % - 320,3 % -
South Korea - 417,5 % 40,8 % - 195,3 % 333,1 % 7,3 %
Belgium 111,3 % - 293,2 % 556,1 % 1139,5 % 368,4 % 167,7 %
Turkey - - 38,9 % 112,8 % 121,9 % 38,9 % 205,2 %
Ireland 115,9 % 332,7 % 342,2 % 197,9 % 163,0 % 61,6 % 135,6 %
France - - - 213,7 % - - -
Canada - 196,3 % - 9,6 % 167,8 % 410,7 % -
Italy - - - - 54,2 % - -
Malaysia - - - - - - -
Australia - 55,2 % - - - 76,6 % -
Vietnam 193,0 % - - - 63,6 % - 52,4 %
Hong Kong 29,8 % 424,7 % 207,8 % 263,0 % 283,4 % 252,8 % 183,9 %
United Kingdom 113,3 % - - 236,1 % 84,2 % 566,2 % 528,0 %
Peru 99,7 % 29,5 % - - - - 138,9 %
Norway - 0,0 % - - 152,0 % - -
Spain 173,1 % 258,7 % 484,3 % 91,8 % - - -
Singapore 698,4 % - - - - - -
Philippines 245,2 % 557,3 % 384,7 % 226,8 % - 144,2 % 171,8 %
Austria 86,6 % - - - 165,5 % 439,6 % 164,6 %
Thailand 192,5 % 394,2 % 74,5 % 14,3 % 38,4 % - 53,9 %
Mexico 14,7 % - 167,6 % - 118,7 % 103,1 % -
Egypt 242,3 % 249,7 % 103,4 % - - - 95,1 %
Indonesia - - - - - 199,8 % 237,7 %
Brazil - - - - - - -
India 24,7 % - - 120,6 % 327,9 % - -
South Africa - - - - - - -
Chile - - - - - 441,4 % -
Colombia 289,3 % 157,5 % - - - 112,6 % 131,0 %
Total 3743,3 % 4594,5 % 3044,6 % 2720,7 % 3633,9 % 4650,6 % 3043,9 %
111
Appendix 76: Half Kelly portfolio weights using a single technical indicator and emerging market countries. The total
number of portfolios is 7. These are derived by using equation 14 and in-sample (1st of September 2011 to 31st of
December 2015) data to optimize.
Country MACD TRB50 TRB150 TRB200 RSI STOCH-D OBV
Taiwan 126,8 % - - 203,6 % 90,7 % - -
China 221,7 % 104,9 % - 51,6 % 206,7 % - 87,9 %
South Korea - 261,5 % 190,5 % - 227,4 % 426,1 % 22,9 %
Turkey - - 67,0 % 135,6 % 140,7 % 27,5 % 185,4 %
Malaysia - - - - - - -
Vietnam 201,5 % - - - 69,1 % - 64,7 %
Peru 108,1 % 70,1 % - - - - 215,3 %
Philippines 284,1 % 579,0 % 366,4 % 257,3 % - 173,6 % 220,1 %
Thailand 251,2 % 356,0 % 73,2 % 38,5 % 66,6 % - 40,5 %
Mexico 36,5 % - 261,7 % - 187,3 % 102,8 % -
Egypt 258,7 % 244,5 % 113,0 % 15,8 % - - 85,3 %
Indonesia - - - - - 156,2 % 191,2 %
Brazil - - - - - - -
India 45,2 % - 64,5 % 123,1 % 311,0 % - -
South Africa - - - - 10,3 % - -
Chile - - - - - 418,0 % 19,2 %
Colombia 234,8 % 137,5 % - - - 158,9 % 221,1 %
Total 1768,4 % 1753,5 % 1136,2 % 825,4 % 1309,9 % 1463,0 % 1353,6 %
112
Appendix 77: Half Kelly portfolio weights using a single technical indicator and developed countries. The total number of
portfolios is 7. These are derived by using equation 14 and in-sample (1st of September 2011 to 31st of December 2015)
data to optimize.
Country MACD TRB50 TRB150 TRB200 RSI STOCH-D OBV
New Zealand - 219,33 % 53,56 % 176,28 % - - -
Netherlands 292,69 % - 255,99 % - 341,35 % - -
Switzerland 383,89 % 580,29 % 486,70 % 77,63 % 0,28 % 781,35 % 353,16 %
Japan 82,90 % - - - 128,44 % - 114,26 %
Sweden - - - - - - 144,52 %
Israel 20,58 % - 25,44 % - 326,97 % 17,78 % -
Germany - 566,80 % - 393,03 % - 381,30 % -
Belgium 180,87 % - 322,57 % 429,26 % 1200,22 % 355,79 % 188,82 %
Ireland 130,14 % 325,14 % 332,38 % 210,02 % 83,15 % 53,57 % 108,29 %
France - - - 254,79 % - - -
Canada - 60,88 % - - - 365,87 % -
Italy - - - - 149,47 % - -
Australia - 157,46 % - - - 110,75 % -
United Kingdom 55,65 % - - 404,84 % 62,74 % 481,29 % 603,33 %
Norway - - - - - - -
Spain 155,02 % 200,82 % 447,26 % 148,12 % - 16,22 % -
Austria 78,70 % - - - 155,23 % 383,90 % 171,38 %
Hong Kong 133,98 % 317,63 % 207,71 % 104,63 % 289,22 % 79,20 % 89,97 %
Singapore 583,50 % - - - - - -
Total 2097,9 % 2428,3 % 2131,6 % 2198,6 % 2737,1 % 3027,0 % 1773,7 %
113
Appendix 78: Fama-French 5-factor regression for single technical indicators per country without using Kelly criterion.
***, **, * represent statistical significance at 1%, 5%,10% level, respectively. The data is from the 1st of September 2011
to the 19th of June 2020.
a b s H r c
Taiwan MACD 0,55 % 0,35*** -0,18 0,24 -0,41 -0,49
Taiwan TRB50 -0,72 %* 0,26*** -0,81*** 0,11 0,43 -0,58
Taiwan TRB150 -0,22 % 0,31*** -0,29 0,58* 0,06 -0,39
Taiwan TRB200 -0,33 % 0,40*** 0,15 0,56 0,18 -0,31
Taiwan RSI 0,74 % 0,56*** 0,49 -0,47 0,05 0,25
Taiwan STOCH-D -0,14 % 0,51*** -0,30 0,08 0,49 -0,18
Taiwan OBV -0,67 % 0,68*** 0,07 0,45 0,58 -0,10
China MACD -0,83 %* 0,39*** -0,90** 0,46 -0,14 -1,14*
China TRB50 -0,17 % 0,27** -0,55* 0,43 0,50 -0,82
China TRB150 -0,43 % 0,56*** -0,36 0,43 0,39 -0,76
China TRB200 -0,38 % 0,37*** -0,04 0,57 -0,16 -0,76
China RSI 0,15 % 0,47*** -0,55* -0,41 -0,30 -0,58
China STOCH-D -0,45 % 0,52*** -0,48 0,39 0,06 -0,77
China OBV 0,19 % 0,30*** -1,12*** 0,17 0,01 -1,02**
New Zealand MACD 0,11 % 0,24* 0,52 -0,35 0,13 -0,13
New Zealand TRB50 0,37 % 0,19* 0,39 0,08 0,12 -0,10
New Zealand TRB150 -0,16 % 0,40*** 0,40 -0,12 0,32 0,23
New Zealand TRB200 0,01 % 0,44*** 0,51 -0,27 0,07 0,46
New Zealand RSI -0,28 % 0,42*** 0,61* 0,34 0,95* -0,09
New Zealand STOCH-D 0,25 % 0,39*** 0,34 0,19 0,23 0,20
New Zealand OBV -0,30 % 0,49*** 0,66* 0,79* 1,09* 0,22
Netherlands MACD 0,14 % 0,49*** -0,28 -0,09 0,23 -0,18
Netherlands TRB50 0,21 % 0,49*** -0,63** -0,66** -0,76 0,42
Netherlands TRB150 -0,09 % 0,58*** -0,50* 0,12 0,28 0,34
Netherlands TRB200 -0,07 % 0,49*** -0,17 0,06 0,04 0,28
Netherlands RSI -0,49 % 0,53*** 0,49* 0,67** 1,36*** -0,63
Netherlands STOCH-D 0,13 % 0,45*** -0,77** 0,54 1,03* 0,29
Netherlands OBV -0,03 % 0,53*** -0,42 0,82** 0,89 -0,57
Switzerland MACD 0,01 % 0,31*** -0,34 -0,04 -0,25 -0,15
Switzerland TRB50 0,08 % 0,50*** -0,57*** -0,91*** -0,76** 0,77***
Switzerland TRB150 0,00 % 0,45*** -0,24 -0,37* 0,04 0,97***
Switzerland TRB200 -0,04 % 0,46*** -0,21 -0,32 0,08 0,99***
Switzerland RSI -0,01 % 0,36*** 0,16 0,09 0,25 0,10
Switzerland STOCH-D -0,16 % 0,23*** -0,66*** -0,02 -0,24 -0,09
Switzerland OBV -0,62 % 0,53*** -0,75** 0,43 0,06 -0,38
Japan MACD -0,17 % 0,28*** -0,36 0,18 0,29 -0,86**
Japan TRB50 -0,02 % 0,26*** -0,28 -0,33 -0,74* -0,08
Japan TRB150 0,07 % 0,28*** 0,35* -0,34 -0,10 0,91***
Japan TRB200 0,01 % 0,30*** 0,18 -0,30 -0,07 0,90***
Japan RSI -0,22 % 0,36*** 0,08 0,60* 1,33*** -0,26
114
Japan STOCH-D -0,07 % 0,51*** 0,27 0,01 -0,73* -0,23
Japan OBV -0,07 % 0,30*** 0,04 0,33 -0,21 -0,65
Sweden MACD -0,08 % 0,52*** -0,31 -0,19 -0,75 -0,74
Sweden TRB50 -0,22 % 0,45*** -0,55* -0,60* -0,66 0,04
Sweden TRB150 -0,35 % 0,44*** -0,04 0,10 0,54 0,84**
Sweden TRB200 -0,47 % 0,42*** -0,12 0,13 0,61 0,79**
Sweden RSI 0,18 % 0,62*** 0,08 0,59 0,65 -0,59
Sweden STOCH-D -0,64 % 0,58*** -1,08** 1,14** 1,06 -0,76
Sweden OBV -0,80 % 0,60*** -0,43 1,43*** 1,76** -0,16
Israel MACD 0,31 % 0,20* 0,15 -0,02 0,13 -0,84**
Israel TRB50 -0,23 % 0,36*** -0,27 -0,55 -0,70 0,11
Israel TRB150 -0,72 %* 0,32*** 0,16 -0,43 -0,13 0,22
Israel TRB200 -0,50 % 0,48*** 0,36 -0,98** -0,82 0,71
Israel RSI -0,03 % 0,56*** 0,86** 0,66 1,04* -0,57
Israel STOCH-D -0,71 % 0,54*** -0,13 0,51 1,00 -0,36
Israel OBV -1,09 %* 0,55*** 0,13 1,36** 1,98** -0,26
Germany MACD -0,32 % 0,36*** -0,08 0,44 -0,21 -1,37**
Germany TRB50 0,19 % 0,48*** -0,78*** -0,59** -0,66 0,39
Germany TRB150 -0,28 % 0,52*** -0,12 -0,07 0,36 1,15***
Germany TRB200 -0,31 % 0,51*** -0,05 -0,07 0,40 1,15***
Germany RSI -0,63 %* 0,64*** 0,32 1,19*** 1,16** -1,48***
Germany STOCH-D -0,23 % 0,54*** -1,08*** 0,61* 0,14 -0,42
Germany OBV 0,14 % 0,66*** -0,52* 0,23 -0,09 -0,16
South Korea MACD -0,57 % 0,51*** -1,05*** -0,08 -0,17 -0,05
South Korea TRB50 -0,46 % 0,45*** -0,67** -0,34 0,67 0,50
South Korea TRB150 -0,46 % 0,46*** 0,23 0,33 0,58 0,25
South Korea TRB200 0,04 % 0,24** -0,15 -0,17 0,44 0,21
South Korea RSI -0,12 % 0,76*** 0,36 -0,38 -0,23 0,08
South Korea STOCH-D -1,45 %** 0,78*** 0,36 0,35 1,04* -0,26
South Korea OBV -1,25 %*** 0,42*** -1,06*** -0,17 0,30 -0,32
Belgium MACD -0,22 % 0,38*** -0,30 -0,18 -0,21 -0,05
Belgium TRB50 0,17 % 0,37*** -0,43 -0,58** -0,58 0,57
Belgium TRB150 -0,14 % 0,51*** -0,10 -0,87*** -0,46 1,25***
Belgium TRB200 -0,18 % 0,54*** -0,18 -0,91*** -0,61 1,35***
Belgium RSI -0,95 %** 0,77*** 0,26 0,39 1,56*** 0,32
Belgium STOCH-D 0,02 % 0,45*** -0,39 -0,36 -0,14 0,35
Belgium OBV -0,54 % 0,59*** -0,05 -0,04 0,00 0,14
Turkey MACD -0,91 % 0,47* -0,28 -0,66 -1,28 0,47
Turkey TRB50 -0,97 % 0,36* -1,05* 0,02 -0,03 0,82
Turkey TRB150 -1,13 %* 0,26* -0,39 -0,12 -0,33 0,62
Turkey TRB200 -1,06 %* 0,32** -0,33 0,31 -0,34 -0,01
Turkey RSI -0,17 % 0,98*** 0,13 -0,80 -1,14 0,64
Turkey STOCH-D -1,00 % 0,39* -0,55 -0,06 -0,93 0,35
Turkey OBV -1,12 % 0,64*** -0,86 0,16 -1,20 0,06
115
Ireland MACD -0,62 % 0,30** 0,05 0,41 -0,11 -1,56***
Ireland TRB50 0,00 % 0,32*** -0,45 -0,34 -0,34 -0,02
Ireland TRB150 -0,29 % 0,39*** 0,25 0,08 0,52 0,82**
Ireland TRB200 -0,33 % 0,38*** 0,28 0,07 0,48 0,82**
Ireland RSI -1,04 %** 0,62*** 0,29 1,67*** 2,07*** -1,96***
Ireland STOCH-D -0,22 % 0,32*** -0,45 -0,01 0,17 -0,48
Ireland OBV -0,48 % 0,27** -0,23 0,09 -0,41 -1,13**
France MACD -0,44 % 0,48*** -0,74** 0,25 0,00 -0,82*
France TRB50 -0,06 % 0,53*** -0,65** -0,65** -0,85* 0,16
France TRB150 0,09 % 0,47*** -0,37 -0,53* -0,22 1,32***
France TRB200 0,08 % 0,47*** -0,38 -0,53* -0,21 1,31***
France RSI -0,75 %** 0,65*** 0,25 1,07*** 1,53*** -0,75*
France STOCH-D 0,15 % 0,57*** -0,82** 0,46 0,49 -0,24
France OBV 0,03 % 0,50*** -1,03*** 0,07 -0,43 -0,65*
Canada MACD 0,50 % 0,35*** 0,03 -0,40 -0,02 0,63
Canada TRB50 0,16 % 0,37*** 0,01 -0,59** -1,29*** 0,39
Canada TRB150 0,15 % 0,28*** 0,27 -0,46* -0,67* 0,88***
Canada TRB200 0,01 % 0,36*** 0,05 -0,60** -0,99** 0,45
Canada RSI -0,47 % 0,56*** 0,65* 0,84** 1,82*** -0,13
Canada STOCH-D 0,25 % 0,44*** 0,18 0,20 0,61 0,45
Canada OBV 0,29 % 0,35*** 0,19 0,52** 0,74* 0,25
Italy MACD 0,21 % 0,64*** -0,70 -0,21 -0,90 -0,41
Italy TRB50 -0,11 % 0,47*** -0,89*** -0,44 -1,03* 0,21
Italy TRB150 -0,24 % 0,67*** -0,54 0,38 0,60 1,19**
Italy TRB200 -0,20 % 0,63*** -0,25 0,22 0,21 1,21**
Italy RSI -1,08 %** 0,77*** -0,37 1,60*** 0,96 -1,63***
Italy STOCH-D -0,97 %** 0,74*** -1,35*** 0,56 0,22 -0,66
Italy OBV -0,90 %** 0,70*** -1,13*** 0,69* -0,12 -1,37***
Malaysia MACD -0,23 % 0,31*** -0,81** -0,73** -1,07** 0,43
Malaysia TRB50 -0,52 % 0,27*** -0,31 -0,40 0,01 0,56
Malaysia TRB150 -0,15 % 0,08 -0,21 -0,12 -0,25 0,09
Malaysia TRB200 -0,23 % 0,07 -0,28 -0,07 -0,21 0,01
Malaysia RSI -0,58 %* 0,52*** 0,20 -0,51** 0,02 0,88**
Malaysia STOCH-D -1,05 %* 0,28** -0,23 0,19 0,71 0,68
Malaysia OBV -0,87 %* 0,30*** -0,86** -0,24 -0,17 0,76
Australia MACD -0,34 % 0,37*** 0,38 0,04 0,23 -0,45
Australia TRB50 -0,28 % 0,24*** 0,09 -0,38 -0,62 0,41
Australia TRB150 -0,08 % 0,33*** 0,22 -0,35 -0,14 0,92**
Australia TRB200 -0,15 % 0,30*** 0,35 -0,53** -0,34 1,07***
Australia RSI -0,42 % 0,73*** 0,98** 0,81* 2,04*** 0,19
Australia STOCH-D -0,83 % 0,59*** 0,98* 1,79*** 2,22** 0,38
Australia OBV -1,34 %* 0,66*** 0,83 2,34*** 2,34** -0,58
Vietnam MACD 0,21 % 0,54*** 0,42 -0,63 -1,12** 0,46
Vietnam TRB50 -0,09 % 0,28*** -0,17 -0,62* 0,29 0,43
116
Vietnam TRB150 -0,65 % 0,27** -0,38 -0,34 0,34 0,31
Vietnam TRB200 -0,63 % 0,36*** -0,35 -0,49 0,29 0,42
Vietnam RSI -0,08 % 0,95*** 1,28*** -0,53 -0,66 0,83
Vietnam STOCH-D -0,17 % 0,56*** 0,86** 0,25 0,18 0,41
Vietnam OBV -0,22 % 0,48*** 0,96*** 0,23 0,39 0,33
Hong Kong MACD 0,22 % 0,39*** 0,13 -0,04 0,07 0,24
Hong Kong TRB50 0,03 % 0,38*** -0,45 -0,35 -0,47 0,22
Hong Kong TRB150 -0,01 % 0,35** -0,03 -0,44 -0,38 0,62
Hong Kong TRB200 -0,35 % 0,39*** 0,23 0,11 0,41 0,65
Hong Kong RSI -0,18 % 0,34** 0,53 0,26 0,00 -0,51
Hong Kong STOCH-D -0,34 % 0,49*** 0,30 0,73* 0,71 -0,14
Hong Kong OBV -0,31 % 0,65*** -0,23 0,59 0,07 -0,13
United Kingdom MACD 0,01 % 0,35*** -0,56* 0,12 -0,57 -0,82*
United Kingdom TRB50 -0,23 % 0,35*** -0,55 -0,35 -1,29** -0,39
United Kingdom TRB150 -0,28 % 0,41*** -0,07 -0,51* -0,21 1,24***
United Kingdom TRB200 -0,37 % 0,40*** -0,10 -0,51* -0,17 1,24***
United Kingdom RSI -0,56 %* 0,68*** 0,18 0,91*** 1,53*** -0,31
United Kingdom STOCH-D -0,71 %* 0,53*** -0,40 0,93*** 1,37*** 0,29
United Kingdom OBV -1,03 %** 0,56*** -0,14 1,27*** 1,19** -0,44
Peru MACD 1,48 %*** 0,45*** -0,40 -0,54 -0,93* 0,63
Peru TRB50 0,28 % 0,43** -0,33 0,12 0,00 0,74
Peru TRB150 0,18 % 0,41** -0,04 -0,23 0,10 0,86
Peru TRB200 0,22 % 0,28** -0,01 -0,15 0,00 0,61
Peru RSI -0,19 % 0,83*** 0,91** 0,07 -0,44 0,04
Peru STOCH-D 1,27 %** 0,66*** 0,50 -0,16 -0,92* 0,86
Peru OBV 1,16 %* 0,56*** 0,10 0,11 -0,46 0,31
Norway MACD 0,18 % 0,44*** 0,78* -0,62 -1,16* 0,25
Norway TRB50 -0,07 % 0,49*** 0,10 -0,64 -1,80*** 0,43
Norway TRB150 0,09 % 0,53*** 0,23 -0,76* -1,00* 1,20**
Norway TRB200 0,36 % 0,36*** -0,13 -0,69* -1,14** 0,74
Norway RSI -0,59 % 0,89*** 1,15*** 0,86** 1,32** 0,17
Norway STOCH-D -0,26 % 0,80*** 0,45 -0,61 -0,26 1,65***
Norway OBV -1,17 % 0,77*** 1,25** 1,55** 1,08 -0,59
Spain MACD -0,54 % 0,41** -0,47 0,59 -0,15 -1,05*
Spain TRB50 -0,29 % 0,46*** -0,91** -0,49 -0,94 0,35
Spain TRB150 -0,08 % 0,40*** -0,40 -0,57** -0,14 1,03***
Spain TRB200 0,16 % 0,29*** -0,49** -0,55** -0,43 0,67**
Spain RSI -0,92 %** 0,78*** -0,23 1,50*** 1,54*** -0,86*
Spain STOCH-D -0,56 % 0,76*** -1,12*** 1,30*** 0,92 -0,67
Spain OBV -0,57 % 0,91*** -0,45 0,86** 0,34 -0,37
Singapore MACD 0,27 % 0,39*** -0,41 -0,16 -0,30 0,16
Singapore TRB50 -0,35 % 0,34*** -0,37 -0,56 -0,68 0,75
Singapore TRB150 0,07 % 0,39*** -0,75** -0,34 -0,51 0,58
Singapore TRB200 -0,16 % 0,42*** -0,53 -0,08 -0,05 0,68
117
Singapore RSI -0,45 % 0,83*** 0,45 0,75* 1,38** 0,56
Singapore STOCH-D -0,20 % 0,73*** -0,16 0,52 0,42 0,90*
Singapore OBV 0,43 % 0,53*** -0,09 -0,18 -0,45 0,62
Philippines MACD -0,43 % 0,30** -0,46 -0,17 -0,59 0,40
Philippines TRB50 -0,59 % 0,23** -0,64** -0,35 0,42 0,39
Philippines TRB150 -0,70 %* 0,17* -0,41 -0,29 0,26 0,37
Philippines TRB200 -0,83 %** 0,20** -0,36 -0,40 0,13 0,65
Philippines RSI -0,35 % 0,55*** 0,78* 0,41 -0,60 -0,24
Philippines STOCH-D -0,21 % 0,44*** -0,03 0,32 -0,63 -0,20
Philippines OBV -0,39 % 0,08 -0,45 -0,23 -0,02 0,27
Austria MACD -0,11 % 0,51*** -0,04 0,24 -0,47 -0,57
Austria TRB50 0,83 %** 0,50*** -0,29 -0,65* -1,09** 0,84*
Austria TRB150 0,36 % 0,55*** -0,11 -0,75** -0,54 1,62***
Austria TRB200 0,68 %* 0,40*** -0,24 -0,71** -0,86* 1,10***
Austria RSI -1,06 %** 0,87*** 0,89** 1,32*** 2,15*** -0,10
Austria STOCH-D 0,05 % 0,49*** -0,54 0,56 0,44 0,16
Austria OBV 0,24 % 0,42** -0,56 -0,13 -0,89 -0,70
Thailand MACD 0,77 %* 0,53*** -0,20 -1,11*** -0,86* 1,56***
Thailand TRB50 0,15 % 0,43*** -0,44 -0,69** -0,05 0,56
Thailand TRB150 0,09 % 0,21** 0,07 -0,07 0,06 0,27
Thailand TRB200 0,06 % 0,20** 0,17 -0,07 -0,03 0,22
Thailand RSI 0,50 % 0,67*** 0,42 -0,46 -0,68 1,66**
Thailand STOCH-D -0,17 % 0,45*** 0,09 -0,16 -0,10 0,93
Thailand OBV -0,33 % 0,33*** -0,26 -0,09 -0,09 0,69
Mexico MACD -0,19 % 0,20 -1,12*** -0,51 -1,69*** -0,12
Mexico TRB50 -0,89 %* 0,47*** -0,57 -0,97** 0,27 1,16*
Mexico TRB150 -0,55 % 0,20** 0,43* 0,28 -0,25 0,08
Mexico TRB200 -1,00 %** 0,30*** -0,10 0,26 -0,33 0,22
Mexico RSI 0,02 % 0,98*** 0,72 0,01 -0,63 0,50
Mexico STOCH-D -0,85 % 0,64*** -0,12 -0,35 0,09 0,56
Mexico OBV -0,89 %* 0,42*** -0,15 -0,28 0,36 1,13**
Egypt MACD -1,15 % 0,82** 1,37 -0,06 0,15 -1,31
Egypt TRB50 -0,73 % 0,30 0,96 -0,15 -0,12 0,01
Egypt TRB150 -0,54 % 0,24 0,34 -0,58 0,03 0,21
Egypt TRB200 0,32 % 0,03 0,14 -0,31 0,17 0,27
Egypt RSI 0,06 % 0,80*** 0,34 -0,74 -0,34 0,73
Egypt STOCH-D -0,94 % 0,35 0,44 -0,59 -0,33 0,07
Egypt OBV -0,26 % 0,38** 0,58 -0,08 0,11 -0,25
Indonesia MACD 0,56 % 0,28** 0,02 -0,52 -0,99* -0,19
Indonesia TRB50 -0,76 %* 0,36*** -0,98*** -0,51 -0,53 0,30
Indonesia TRB150 -0,54 % 0,40*** -0,84** -0,36 0,35 0,78
Indonesia TRB200 -0,16 % 0,17* -0,31 -0,10 -0,03 0,05
Indonesia RSI -0,53 % 1,02*** 1,55*** 0,10 -0,27 0,27
Indonesia STOCH-D -0,11 % 0,29** -0,07 -0,11 0,15 0,10
118
Indonesia OBV 0,00 % 0,47*** 0,20 0,11 -1,27** -0,36
Brazil MACD 1,17 % 0,53*** -1,16** 0,32 -1,77** 0,60
Brazil TRB50 -0,45 % 0,46** -0,68 0,11 -0,17 1,34
Brazil TRB150 -0,25 % 0,70*** -1,23** -0,19 -0,04 1,92*
Brazil TRB200 -0,57 % 0,53*** -1,26** -0,04 -0,09 1,00
Brazil RSI 0,59 % 0,62*** 1,87*** 1,20* -1,08 0,21
Brazil STOCH-D -2,69 %** 1,49*** 2,06** 1,53 -0,17 -0,31
Brazil OBV -1,92 %* 1,18*** 0,19 1,17 -1,13 0,91
India MACD -0,49 % 0,17 -0,58 0,12 -1,87** -1,14
India TRB50 -0,34 % -0,04 -0,22 0,44 -1,09* -1,17
India TRB150 -0,31 % 0,18* -0,56* 0,24 -0,18 -0,22
India TRB200 -0,19 % 0,16 -0,48 0,28 -0,11 -0,61
India RSI 0,51 % 0,46*** 1,55*** 1,02** -0,42 -0,85
India STOCH-D -0,68 % 0,02 -0,18 0,52 -1,57** -1,11
India OBV -1,35 %* 0,21 0,24 0,94* -1,51** -1,43
South Africa MACD -1,00 % 0,66*** -0,70 -1,48*** -0,53 1,17
South Africa TRB50 -1,13 %* 0,37*** -0,85** -0,73* -0,31 1,02
South Africa TRB150 -0,52 % 0,41*** -0,38 -0,80** -0,07 0,95
South Africa TRB200 -0,09 % 0,33*** -0,21 -0,91** -0,34 1,06*
South Africa RSI 0,45 % 1,02*** 0,96 -0,03 -0,52 0,33
South Africa STOCH-D -1,01 % 1,18*** -0,54 -0,56 0,20 1,59*
South Africa OBV -1,30 % 1,10*** 0,63 0,29 0,02 0,83
Chile MACD 0,53 % 0,51*** -0,34 -0,67 0,58 1,50*
Chile TRB50 -0,49 % 0,19 -0,60 0,14 -0,62 -0,02
Chile TRB150 0,11 % 0,35** -0,31 -0,08 -0,15 0,72
Chile TRB200 -0,10 % 0,30* -0,22 -0,06 -0,04 0,28
Chile RSI -1,13 %* 1,14*** 0,84* -0,59 1,91*** 2,59***
Chile STOCH-D 0,06 % 1,26*** 0,78 -0,32 1,10 2,17**
Chile OBV 0,69 % 0,65*** -0,21 -0,15 0,91 1,48**
Colombia MACD 0,11 % 0,39*** -0,75* -0,48 0,57 0,78
Colombia TRB50 -0,75 % 0,40*** -0,53 -0,46 -0,48 0,88
Colombia TRB150 -0,82 % 0,36*** -0,55 -0,51 -0,25 0,95
Colombia TRB200 -0,17 % 0,25** -0,09 -0,56 0,06 0,57
Colombia RSI -0,62 % 1,14*** 1,03* -0,07 0,57 0,62
Colombia STOCH-D 0,48 % 0,60*** -0,66* -0,63 1,28** 1,08*
Colombia OBV 0,37 % 0,57*** -0,41 -0,42 -0,20 0,12
119
Appendix 79: Descriptive statistics of the data set. The data is from both in-sample (1st of September 2011 to 31st of
December 2015) and out-of-sample (4th of January 2016 to 19th of June 2020) periods and total period (1st of September
2011 to 19th of June 2020).
Observation
Average
daily
return
Standard
deviation Max Median Min Skewness
Excess
Kurtosis
Taiwan
Total 2214 0,02 % 1,3 % 6,7 % 0,06 % -10,9 % -0,09 5,8
Out 1124 0,05 % 1,3 % 6,7 % 0,11 % -10,9 % -0,14 9,0
In 1090 0,00 % 1,2 % 4,4 % 0,00 % -6,1 % 0,00 1,5
China
Total 2214 0,03 % 1,5 % 6,6 % 0,04 % -9,6 % -0,03 2,9
Out 1124 0,04 % 1,4 % 5,7 % 0,08 % -9,6 % -0,09 3,3
In 1090 0,02 % 1,5 % 6,6 % 0,01 % -6,6 % 0,01 2,5
South Korea
Total 2214 0,01 % 1,5 % 12,4 % 0,05 % -15,8 % -0,07 10,8
Out 1124 0,03 % 1,6 % 12,4 % 0,07 % -15,8 % -0,08 13,6
In 1090 0,00 % 1,4 % 6,2 % 0,01 % -8,6 % -0,03 3,2
Turkey
Total 2214 -0,01 % 2,1 % 11,3 % 0,04 % -14,5 % -0,08 3,3
Out 1124 -0,02 % 2,1 % 11,3 % 0,04 % -14,5 % -0,08 4,6
In 1090 -0,01 % 2,0 % 9,6 % 0,05 % -8,3 % -0,09 1,4
Malaysia
Total 2214 -0,03 % 1,3 % 7,4 % 0,00 % -26,5 % -0,06 75,6
Out 1124 -0,01 % 1,3 % 7,4 % 0,00 % -10,7 % -0,02 13,5
In 1090 -0,05 % 1,4 % 6,3 % 0,00 % -26,5 % -0,10 116,2
Vietnam
Total 2214 0,00 % 1,6 % 8,1 % 0,00 % -10,7 % -0,01 3,8
Out 1124 0,00 % 1,5 % 8,1 % 0,00 % -10,7 % 0,01 6,9
In 1090 -0,01 % 1,7 % 6,4 % -0,05 % -8,1 % 0,06 1,8
Peru
Total 2214 -0,01 % 1,3 % 10,3 % 0,00 % -11,8 % -0,02 10,7
Out 1124 0,04 % 1,4 % 10,3 % 0,07 % -11,8 % -0,06 13,9
In 1090 -0,06 % 1,2 % 7,4 % -0,07 % -5,9 % 0,02 3,1
Philippines
Total 2214 0,02 % 1,5 % 11,1 % 0,00 % -19,4 % 0,03 26,2
Out 1124 0,00 % 1,7 % 11,1 % 0,00 % -19,4 % -0,01 36,0
In 1090 0,04 % 1,4 % 7,2 % 0,03 % -8,0 % 0,02 3,8
Thailand
Total 2214 0,01 % 1,5 % 12,3 % 0,05 % -17,2 % -0,07 15,0
Out 1124 0,03 % 1,5 % 12,3 % 0,04 % -17,2 % -0,02 27,9
In 1090 0,00 % 1,5 % 8,2 % 0,05 % -9,0 % -0,10 3,5
Mexico
Total 2214 -0,01 % 1,6 % 8,2 % -0,03 % -15,3 % 0,04 8,3
Out 1124 -0,02 % 1,8 % 8,2 % -0,04 % -15,3 % 0,03 8,9
120
In 1090 -0,01 % 1,3 % 5,0 % -0,03 % -7,1 % 0,06 1,4
Egypt
Total 2201 -0,02 % 1,8 % 13,3 % 0,01 % -18,6 % -0,06 10,9
Out 1117 -0,03 % 1,7 % 13,3 % 0,03 % -18,6 % -0,10 19,1
In 1084 -0,01 % 1,8 % 12,3 % 0,00 % -8,1 % -0,02 4,2
Indonesia
Total 2214 -0,01 % 1,8 % 13,4 % -0,03 % -13,3 % 0,03 7,3
Out 1124 0,01 % 1,8 % 13,4 % 0,04 % -13,3 % -0,06 12,0
In 1090 -0,03 % 1,8 % 8,6 % -0,08 % -10,3 % 0,08 3,1
Brazil
Total 2214 -0,01 % 2,3 % 17,6 % 0,02 % -23,1 % -0,04 11,9
Out 1124 0,07 % 2,6 % 17,6 % 0,17 % -23,1 % -0,12 13,0
In 1090 -0,09 % 1,8 % 6,9 % -0,12 % -6,8 % 0,05 0,8
India
Total 2214 0,01 % 1,6 % 8,9 % 0,06 % -22,0 % -0,09 18,3
Out 1124 0,00 % 1,6 % 8,9 % 0,08 % -22,0 % -0,15 38,7
In 1090 0,01 % 1,7 % 6,8 % 0,05 % -6,3 % -0,06 1,1
South Africa
Total 2214 -0,01 % 2,0 % 10,1 % 0,03 % -14,8 % -0,06 4,9
Out 1124 0,01 % 2,2 % 10,1 % 0,07 % -14,8 % -0,09 5,6
In 1090 -0,02 % 1,7 % 8,9 % -0,02 % -6,6 % 0,00 1,8
Chile
Total 2214 -0,03 % 1,5 % 10,7 % -0,03 % -15,6 % -0,01 13,4
Out 1124 -0,01 % 1,6 % 10,7 % 0,02 % -15,6 % -0,06 16,6
In 1090 -0,06 % 1,3 % 5,9 % -0,07 % -7,7 % 0,02 2,8
Colombia
Total 2214 -0,04 % 1,6 % 14,5 % 0,00 % -15,4 % -0,08 19,0
Out 1124 0,00 % 1,8 % 14,5 % 0,00 % -15,4 % -0,01 21,7
In 1090 -0,08 % 1,4 % 7,3 % -0,09 % -7,0 % 0,02 3,9
New Zealand
Total 2214 0,03 % 1,3 % 15,7 % 0,04 % -15,1 % -0,01 25,5
Out 1124 0,04 % 1,4 % 15,7 % 0,08 % -15,1 % -0,09 33,9
In 1090 0,02 % 1,1 % 4,9 % 0,00 % -4,9 % 0,05 1,7
Netherland
Total 2214 0,03 % 1,3 % 7,4 % 0,08 % -10,4 % -0,11 8,7
Out 1124 0,04 % 1,2 % 7,4 % 0,08 % -10,4 % -0,11 15,1
In 1090 0,03 % 1,3 % 6,0 % 0,09 % -7,4 % -0,12 3,0
Switzerland
Total 2214 0,03 % 1,1 % 7,8 % 0,06 % -10,5 % -0,09 13,0
Out 1124 0,03 % 1,1 % 7,8 % 0,07 % -10,5 % -0,12 20,7
In 1090 0,03 % 1,0 % 4,8 % 0,03 % -6,4 % -0,02 3,4
Japan
Total 2214 0,02 % 1,1 % 6,9 % 0,07 % -9,8 % -0,12 6,4
Out 1124 0,02 % 1,1 % 6,9 % 0,06 % -9,8 % -0,11 11,1
In 1090 0,03 % 1,1 % 5,0 % 0,08 % -5,8 % -0,15 2,1
121
Sweden
Total 2214 0,02 % 1,5 % 9,1 % 0,06 % -12,1 % -0,08 8,6
Out 1124 0,02 % 1,5 % 9,1 % 0,07 % -12,1 % -0,10 14,1
In 1090 0,02 % 1,5 % 8,3 % 0,03 % -7,5 % -0,03 3,5
Israel
Total 2214 0,01 % 1,2 % 9,3 % 0,04 % -12,2 % -0,06 14,5
Out 1124 0,02 % 1,3 % 9,3 % 0,07 % -12,2 % -0,12 21,1
In 1090 0,01 % 1,1 % 5,6 % 0,01 % -5,5 % 0,01 3,4
Germany
Total 2214 0,02 % 1,4 % 10,8 % 0,06 % -12,7 % -0,09 10,2
Out 1124 0,01 % 1,4 % 10,8 % 0,07 % -12,7 % -0,12 18,4
In 1090 0,03 % 1,5 % 8,2 % 0,04 % -7,4 % -0,02 3,5
Belgium
Total 2214 0,02 % 1,2 % 7,7 % 0,05 % -13,2 % -0,08 14,4
Out 1124 0,00 % 1,3 % 7,7 % 0,05 % -13,2 % -0,11 24,3
In 1090 0,04 % 1,2 % 5,7 % 0,06 % -5,5 % -0,05 2,5
Ireland
Total 2198 0,04 % 1,4 % 7,5 % 0,07 % -13,4 % -0,06 13,0
Out 1124 0,00 % 1,4 % 7,2 % 0,05 % -13,4 % -0,11 20,1
In 1074 0,08 % 1,3 % 7,5 % 0,09 % -5,6 % 0,00 2,9
Canada
Total 2214 0,00 % 1,2 % 12,9 % 0,07 % -13,3 % -0,16 23,1
Out 1124 0,02 % 1,3 % 12,9 % 0,07 % -13,3 % -0,10 31,2
In 1090 -0,02 % 1,1 % 5,1 % 0,04 % -5,3 % -0,16 2,1
France
Total 2214 0,02 % 1,4 % 9,1 % 0,07 % -12,7 % -0,10 11,3
Out 1124 0,02 % 1,4 % 9,1 % 0,07 % -12,7 % -0,11 21,2
In 1090 0,02 % 1,5 % 8,0 % 0,04 % -6,7 % -0,05 3,0
Italy
Total 2214 0,01 % 1,8 % 11,2 % 0,07 % -15,6 % -0,11 9,7
Out 1124 0,00 % 1,7 % 11,2 % 0,07 % -15,6 % -0,12 20,4
In 1090 0,02 % 1,9 % 8,4 % 0,09 % -9,4 % -0,12 2,2
Australia
Total 2214 0,00 % 1,5 % 14,2 % 0,04 % -16,1 % -0,08 19,0
Out 1124 0,01 % 1,7 % 14,2 % 0,05 % -16,1 % -0,06 24,9
In 1090 -0,01 % 1,4 % 7,3 % 0,02 % -7,9 % -0,07 3,0
Hong Kong
Total 2214 0,02 % 1,2 % 6,6 % 0,05 % -9,4 % -0,09 5,2
Out 1124 0,01 % 1,2 % 6,6 % 0,08 % -9,4 % -0,17 7,1
In 1090 0,02 % 1,2 % 5,4 % 0,05 % -6,2 % -0,07 2,9
United Kingdom
Total 2214 0,00 % 1,2 % 11,5 % 0,06 % -12,0 % -0,14 16,6
Out 1124 -0,01 % 1,4 % 11,5 % 0,06 % -12,0 % -0,16 21,9
In 1090 0,00 % 1,1 % 4,7 % 0,05 % -5,7 % -0,13 2,3
Norway
122
Total 2214 0,00 % 1,6 % 9,1 % 0,07 % -13,8 % -0,14 7,3
Out 1124 0,01 % 1,6 % 9,1 % 0,07 % -13,8 % -0,11 12,6
In 1090 -0,02 % 1,6 % 6,3 % 0,07 % -7,9 % -0,16 1,9
Spain
Total 2214 -0,01 % 1,6 % 8,9 % 0,07 % -16,3 % -0,14 11,6
Out 1124 -0,01 % 1,6 % 8,9 % 0,06 % -16,3 % -0,14 25,3
In 1090 -0,01 % 1,7 % 7,6 % 0,08 % -7,1 % -0,14 2,1
Singapore
Total 2214 -0,01 % 1,2 % 7,6 % 0,00 % -9,8 % -0,02 7,7
Out 1124 0,00 % 1,2 % 7,6 % 0,04 % -9,8 % -0,11 9,8
In 1090 -0,02 % 1,1 % 6,2 % 0,00 % -5,2 % -0,04 3,8
Austria
Total 2214 0,00 % 1,5 % 8,2 % 0,07 % -15,3 % -0,13 13,9
Out 1124 0,01 % 1,5 % 6,3 % 0,08 % -15,3 % -0,14 23,0
In 1090 -0,01 % 1,5 % 8,2 % 0,06 % -8,0 % -0,13 3,3
